1. The paper proposes a hybrid model of Swin Transformer and CNN for facial emotion recognition (FER). Swin Transformer is a variant of Vision Transformer that uses shifted windows to allow for local self-attention within windows, improving efficiency.
2. Current Transformer models struggle with computer vision tasks due to the large scale of visual entities and high-resolution images compared to text. The proposed hybrid model addresses these issues.
3. Experiments show the hybrid Swin Transformer and CNN model outperforms ViT/DeiT and ResNet baselines on image classification, object detection, FER and semantic segmentation, while having comparable latency.
IRJET- Extension to Visual Information Narrator using Neural NetworkIRJET Journal
The document describes a proposed extension to a visual information narrator application using neural networks. The researchers developed an Android application that can generate image captions on inexpensive mobile hardware in real-time for visually impaired users. They trained an image captioning model using TensorFlow on the FLICKR dataset and implemented the model in an Android app to demonstrate real-world applicability and optimizations for low-cost hardware performance. The system uses an encoder-decoder neural network architecture that encodes an input image into a fixed-length vector using a convolutional neural network, then decodes the vector into a natural language description.
IRJET- Visual Information Narrator using Neural NetworkIRJET Journal
The document describes a neural network model developed by students to generate descriptions of images in natural language for visually impaired people using mobile devices. The model was trained on the Flickr dataset using TensorFlow and can run efficiently on low-end mobile hardware. It uses a convolutional neural network to extract visual features from images, which are then input to a recurrent neural network to generate descriptive text that is output via text-to-speech. The students compared their model's performance and speed on CPU and GPU systems and found it can provide real-time descriptions to help visually impaired users understand their surroundings using mobile devices.
leewayhertz.com-HOW IS A VISION TRANSFORMER MODEL ViT BUILT AND IMPLEMENTED.pdfrobertsamuel23
Recent years have seen deep learning completely transform computer vision and image
processing. Convolutional neural networks (CNNs) have been the driving force behind
this transformation due to their ability to efficiently process large amounts of data,
enabling the extraction of even the smallest image features.
IRJET- Automatic Data Collection from Forms using Optical Character RecognitionIRJET Journal
1) The document presents an automated system for collecting user data from paper forms using optical character recognition (OCR).
2) It involves scanning paper forms, segmenting the user input fields, performing OCR on the input text using a convolutional recurrent neural network model, and updating the data to a database.
3) This system aims to reduce the time and effort required to manually collect and process form data compared to current methods.
IRJET - Gender Recognition from Facial ImagesIRJET Journal
This document discusses gender recognition from facial images using a Wide Residual Network model. The model is trained on a dataset from Kaggle to predict gender from live video stream faces detected by a webcam. When a male face is detected, it draws a red border and sounds an alarm, as the purpose is for male-restricted areas video surveillance. It preprocesses detected faces before predicting gender with the WideResNet model, which reduces depth and increases width compared to standard residual networks for faster training. Experimental results found it achieved good performance for male restricted area video monitoring.
This document reviews object detection techniques using convolutional neural networks (CNNs). It begins with introducing object detection and CNNs. It then discusses the problem of object detection in computer vision and the need for more precise and accurate detection systems. The majority of the document reviews eight previous works that developed algorithms to improve object detection systems, including R-CNN and approaches using K-SVD, deep equilibrium models, non-local networks, transformers, and selective kernel networks. It evaluates these approaches and their abilities to achieve high detection rates while requiring fewer computations or model parameters. The document provides an overview of recent research aiming to advance CNN-based object detection.
IRJET- Face Recognition using Landmark Estimation and Convolution Neural NetworkIRJET Journal
This document summarizes a research paper on face recognition using landmark estimation and convolutional neural networks. The researchers used the LFW dataset to test their system. They first used HOG and SVM for face recognition, achieving 85% accuracy. They then used CNN for further improvement. Keypoints were detected using landmark estimation for face normalization before inputting faces into the CNN. Various CNN architectures and hyperparameters were tested. The best performing model achieved over 95% accuracy on the LFW dataset, demonstrating the effectiveness of the proposed method.
IRJET- Extension to Visual Information Narrator using Neural NetworkIRJET Journal
The document describes a proposed extension to a visual information narrator application using neural networks. The researchers developed an Android application that can generate image captions on inexpensive mobile hardware in real-time for visually impaired users. They trained an image captioning model using TensorFlow on the FLICKR dataset and implemented the model in an Android app to demonstrate real-world applicability and optimizations for low-cost hardware performance. The system uses an encoder-decoder neural network architecture that encodes an input image into a fixed-length vector using a convolutional neural network, then decodes the vector into a natural language description.
IRJET- Visual Information Narrator using Neural NetworkIRJET Journal
The document describes a neural network model developed by students to generate descriptions of images in natural language for visually impaired people using mobile devices. The model was trained on the Flickr dataset using TensorFlow and can run efficiently on low-end mobile hardware. It uses a convolutional neural network to extract visual features from images, which are then input to a recurrent neural network to generate descriptive text that is output via text-to-speech. The students compared their model's performance and speed on CPU and GPU systems and found it can provide real-time descriptions to help visually impaired users understand their surroundings using mobile devices.
leewayhertz.com-HOW IS A VISION TRANSFORMER MODEL ViT BUILT AND IMPLEMENTED.pdfrobertsamuel23
Recent years have seen deep learning completely transform computer vision and image
processing. Convolutional neural networks (CNNs) have been the driving force behind
this transformation due to their ability to efficiently process large amounts of data,
enabling the extraction of even the smallest image features.
IRJET- Automatic Data Collection from Forms using Optical Character RecognitionIRJET Journal
1) The document presents an automated system for collecting user data from paper forms using optical character recognition (OCR).
2) It involves scanning paper forms, segmenting the user input fields, performing OCR on the input text using a convolutional recurrent neural network model, and updating the data to a database.
3) This system aims to reduce the time and effort required to manually collect and process form data compared to current methods.
IRJET - Gender Recognition from Facial ImagesIRJET Journal
This document discusses gender recognition from facial images using a Wide Residual Network model. The model is trained on a dataset from Kaggle to predict gender from live video stream faces detected by a webcam. When a male face is detected, it draws a red border and sounds an alarm, as the purpose is for male-restricted areas video surveillance. It preprocesses detected faces before predicting gender with the WideResNet model, which reduces depth and increases width compared to standard residual networks for faster training. Experimental results found it achieved good performance for male restricted area video monitoring.
This document reviews object detection techniques using convolutional neural networks (CNNs). It begins with introducing object detection and CNNs. It then discusses the problem of object detection in computer vision and the need for more precise and accurate detection systems. The majority of the document reviews eight previous works that developed algorithms to improve object detection systems, including R-CNN and approaches using K-SVD, deep equilibrium models, non-local networks, transformers, and selective kernel networks. It evaluates these approaches and their abilities to achieve high detection rates while requiring fewer computations or model parameters. The document provides an overview of recent research aiming to advance CNN-based object detection.
IRJET- Face Recognition using Landmark Estimation and Convolution Neural NetworkIRJET Journal
This document summarizes a research paper on face recognition using landmark estimation and convolutional neural networks. The researchers used the LFW dataset to test their system. They first used HOG and SVM for face recognition, achieving 85% accuracy. They then used CNN for further improvement. Keypoints were detected using landmark estimation for face normalization before inputting faces into the CNN. Various CNN architectures and hyperparameters were tested. The best performing model achieved over 95% accuracy on the LFW dataset, demonstrating the effectiveness of the proposed method.
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
Artificial Intelligence for Vision: A walkthrough of recent breakthroughsNikolas Markou
we embark on a quest to uncover the fascinating evolution of computer vision, from humble beginnings to the cutting-edge marvels of Vision Transformers.
This document summarizes a research paper on Fashion AI. It proposes a new Group Decreasing Network (GroupDNet) that uses group convolutions in the generator and gradually reduces the percentage of groups in the decoder's convolutions. This allows the model to have more control over generating images from semantic labels and produce high-quality, multi-modal outputs. The paper describes GroupDNet's architecture, compares it to other approaches like using multiple generators, and shows it outperforms other methods on challenging datasets based on metrics like FID and mIoU. Potential applications discussed include mixed fashion styles, semantic manipulation, and tracking fashion trends over time. The conclusion discusses GroupDNet's performance but notes room for improving computational efficiency
IRJET- Semantic Segmentation using Deep LearningIRJET Journal
The document discusses semantic image segmentation using deep learning techniques. It summarizes several state-of-the-art semantic segmentation models like U-Net, Dilated U-Net, PSPNet, Fully Convolutional DenseNets, Global Convolutional Network (GCN), DeepLabV3, and proposes an optimized FRRN model. It implements these models on the CamVid dataset and evaluates their performance using the intersection-over-union score, finding that the optimized FRRN model achieves a score of 0.87.
Gesture Recognition System using Computer VisionIRJET Journal
This document presents a gesture recognition system using computer vision and convolutional neural networks. It discusses developing classifiers to recognize hand gestures and facial expressions. A dataset of 87,000 images is used to train models to classify 26 letters of the American Sign Language alphabet, as well as additional classes for space, delete and nothing. The models are trained using transfer learning with MobileNet, achieving validation accuracies of over 90% for hand gesture classification and implementing a system that recognizes and translates gestures in real-time. It concludes the paper developed robust models for American Sign Language translation and facial expression recognition using CNNs.
IRJET- A Vision based Hand Gesture Recognition System using Convolutional...IRJET Journal
This document describes a vision-based hand gesture recognition system using convolutional neural networks. The system captures images of hand gestures using a camera, pre-processes the images, and classifies the gestures using a CNN model. The CNN architecture includes convolutional layers, max pooling layers, dropout layers, and fully connected layers. The system was trained on a dataset of images representing 7 different hand gestures. Testing achieved over 90% accuracy in recognizing the gestures. This vision-based approach allows for natural human-computer interaction without physical devices.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
IRJET- An Efficient VLSI Architecture for 3D-DWT using Lifting SchemeIRJET Journal
This document proposes an efficient VLSI architecture for 3D discrete wavelet transform (DWT) using the lifting scheme. The lifting scheme implementation of DWT has lower area, power consumption and computational complexity compared to convolution-based DWT. The proposed architecture achieves reductions in total area and power compared to existing convolution DWT and discrete cosine transform architectures. It evaluates the performance in terms of area analysis, timing reports, and output matrices after 1D, 2D and 3D DWT using both convolution and lifting schemes. The results show that the lifting scheme provides better compression performance with less area and delay.
This document discusses comparing the performance of different convolutional neural networks (CNNs) when trained on large image datasets using Apache Spark. It summarizes the datasets used - CIFAR-10 and ImageNet - and preprocessing done to standardize image sizes. It then provides an overview of CNN architecture, including convolutional layers, pooling layers, and fully connected layers. Finally, it introduces SparkNet, a framework that allows training deep networks using Spark by wrapping Caffe and providing tools for distributed deep learning on Spark. The goal is to see if SparkNet can provide faster training times compared to a single machine by distributing training across a cluster.
IMAGE CAPTIONING USING TRANSFORMER: VISIONAIDIRJET Journal
The document proposes a new image captioning model called VisionAid that aims to address several issues with existing approaches. It conducts a literature review of transformer-based image captioning methods to identify solutions. VisionAid incorporates grid-level feature extraction, augmented training data diversity using BERT embeddings, and a combination of normalized self-attention and geometric self-attention to better model object relationships while avoiding internal covariate shift issues. The model aims to generate more accurate and diverse captions by leveraging techniques from various transformer models discussed in the literature review.
CAR DAMAGE DETECTION USING DEEP LEARNINGIRJET Journal
This document discusses using deep learning techniques for car damage detection and classification. Specifically, it proposes using a Mask R-CNN model with transfer learning. As there was no publicly available car damage dataset, the authors created their own dataset of 970 images with various damage types labeled. They experimented with different deep learning approaches, finding that transfer learning combined with Mask R-CNN performed best for this task. The proposed methodology involves collecting and labeling images, applying the Mask R-CNN model with transferred features from pre-trained networks, and predicting the damage results.
VIDEO BASED SIGN LANGUAGE RECOGNITION USING CNN-LSTMIRJET Journal
This document presents a proposed method for video-based sign language recognition using convolutional neural networks (CNN) and long short-term memory (LSTM). The method uses CNN to extract spatial features from video frames of sign language and LSTM to analyze the temporal characteristics of the frames to recognize the sign. Color segmentation is used to isolate the hands from video frames by detecting colored gloves worn by the signer. CNN is trained on spatial features from frames to classify signs, and LSTM is used to analyze the sequential features from CNN to recognize signs in full videos. The proposed method achieved 94% accuracy on sign recognition in testing.
Recognition and Detection of Real-Time Objects Using Unified Network of Faste...dbpublications
Region based proposals regularly depend on the features which are economical prudent derivation schemes. The proposed network includesa Region Proposal Network (RPN) which accepts a picture of any size as input and yields an arrangement of rectangular object recommendations, which includes an objectness score. The RPN is prepared end-to-end to produce great quality object recommendations, which are then utilized by Faster R-CNN for object recognition. Further the trained RPN is additionally converged with Faster R-CNN into a solitary system by sharing their convolutional highlights utilizing the as of late famous wording of neural systems with "attention" techniques and the RPN segment advises the brought together system where to look for the object in input. This strategy empowers a unified, profound learning region based proposals for object detection system. The scholarly RPN additionally enhances area proposition quality and accordingly increases the accuracy in object recognition.
Photo Editing And Sharing Web Application With AI- Assisted FeaturesIRJET Journal
The document describes a web application for photo editing and sharing that utilizes generative adversarial networks (GANs) and other machine learning techniques to provide AI-assisted editing features. Specifically, it uses StyleGAN to allow users to semantically edit image attributes like age, pose, and smile without needing expert photo editing skills. The application was developed with Python-Django and its AI features include encoding images into latent spaces, editing the latent vectors to modify attributes, and generating high resolution images. The goal is to make image editing more accessible while producing high fidelity results.
3rd Workshop on Advances in Slicing for Softwarized Infrastructures (S4SI 2020)
Panel: Network Slicing is multifaceted but does its approach and understanding need to be fragmented?
Abstract: Network Slicing keeps growing in significance in the academic and industrial communities. Network Slicing can be defined from different functional or behavioral perspectives, as well as from different viewpoints depending on the stakeholder (e.g., verticals, solution providers, infrastructure owners) and the technical domain (e.g. cloud data centers, radio access, packet/optical transport networks). Standardization bodies and open source projects are being involved in some forms of network slicing support. How far are these views from each other? Is fragmentation leading to incompatible approaches or is there some hope of convergence, at least at conceptual levels? What is the next frontier in Network Slicing? These and other questions will be thrown to our panel experts after introducing their lightning viewpoints.
Moderator: Christian Esteve Rothenberg, University of Campinas, Brazil
Panel Members
Constantine Polychronopoulos, Juniper Networks, USA
Uma Chunduri, Futurewei, USA
Slawomir Kuklinski, Orange Poland and Warsaw University of Technology, Poland
Stuart Clayman, University College London, UK
Augusto Venancio Neto, Federal University of Rio Grande do Norte, Brazil
IRJET- Intelligent Character Recognition of Handwritten CharactersIRJET Journal
This document summarizes research on intelligent character recognition of handwritten characters using neural networks. It discusses how neural networks can be trained on feature vectors extracted from images to accurately recognize (up to 95%) handwritten alphanumeric characters. The proposed system segments images into characters, extracts features like intersections and endpoints, trains a neural network on feature vectors, and then uses the trained network to recognize new characters. It achieved high accuracy after training on a large dataset of 400 samples. The system automatically transfers recognized text to an Excel sheet.
Car Steering Angle Prediction Using Deep LearningIRJET Journal
This document discusses two deep learning models for predicting steering angles of a self-driving car from camera images. The first model uses 3D convolutional layers followed by LSTM layers to incorporate temporal information. The second model uses transfer learning with pre-trained CNN models. Both models were tested in simulation and showed potential for real-world autonomous driving applications by accurately predicting steering angles from camera images alone.
The document proposes combining the Inception neural network architecture with residual connections. It first reviews the Inception and ResNet architectures individually. It then describes new Inception-ResNet models that incorporate residual connections into the Inception blocks. The models are tested on the ImageNet dataset, with an ensemble of Inception-v4 and Inception-ResNet-v2 achieving 3.1% top-5 error, setting a new state-of-the-art. Detailed architectures are provided for the new Inception-v4 and Inception-ResNet variants.
FACE COUNTING USING OPEN CV & PYTHON FOR ANALYZING UNUSUAL EVENTS IN CROWDSIRJET Journal
The document discusses face counting using OpenCV and Python by analyzing unusual events in crowds. It proposes using the Haar cascade algorithm for face detection and counting. Feature extraction is performed using gray-level co-occurrence matrix (GLCM) to extract texture and edge features. Discriminant analysis is then used to differentiate between samples accurately. The system aims to correctly detect and count faces in images using Python tools like OpenCV for digital image processing tasks and feature extraction algorithms like GLCM and discrete wavelet transform (DWT). It is intended to have good recognition accuracy compared to previous methods.
How can you handle defects? If you are in a factory, production can produce objects with defects. Or values from sensors can tell you over time that some values are not "normal". What can you do as a developer (not a Data Scientist) with .NET o Azure to detect these anomalies? Let's see how in this session.
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
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
Artificial Intelligence for Vision: A walkthrough of recent breakthroughsNikolas Markou
we embark on a quest to uncover the fascinating evolution of computer vision, from humble beginnings to the cutting-edge marvels of Vision Transformers.
This document summarizes a research paper on Fashion AI. It proposes a new Group Decreasing Network (GroupDNet) that uses group convolutions in the generator and gradually reduces the percentage of groups in the decoder's convolutions. This allows the model to have more control over generating images from semantic labels and produce high-quality, multi-modal outputs. The paper describes GroupDNet's architecture, compares it to other approaches like using multiple generators, and shows it outperforms other methods on challenging datasets based on metrics like FID and mIoU. Potential applications discussed include mixed fashion styles, semantic manipulation, and tracking fashion trends over time. The conclusion discusses GroupDNet's performance but notes room for improving computational efficiency
IRJET- Semantic Segmentation using Deep LearningIRJET Journal
The document discusses semantic image segmentation using deep learning techniques. It summarizes several state-of-the-art semantic segmentation models like U-Net, Dilated U-Net, PSPNet, Fully Convolutional DenseNets, Global Convolutional Network (GCN), DeepLabV3, and proposes an optimized FRRN model. It implements these models on the CamVid dataset and evaluates their performance using the intersection-over-union score, finding that the optimized FRRN model achieves a score of 0.87.
Gesture Recognition System using Computer VisionIRJET Journal
This document presents a gesture recognition system using computer vision and convolutional neural networks. It discusses developing classifiers to recognize hand gestures and facial expressions. A dataset of 87,000 images is used to train models to classify 26 letters of the American Sign Language alphabet, as well as additional classes for space, delete and nothing. The models are trained using transfer learning with MobileNet, achieving validation accuracies of over 90% for hand gesture classification and implementing a system that recognizes and translates gestures in real-time. It concludes the paper developed robust models for American Sign Language translation and facial expression recognition using CNNs.
IRJET- A Vision based Hand Gesture Recognition System using Convolutional...IRJET Journal
This document describes a vision-based hand gesture recognition system using convolutional neural networks. The system captures images of hand gestures using a camera, pre-processes the images, and classifies the gestures using a CNN model. The CNN architecture includes convolutional layers, max pooling layers, dropout layers, and fully connected layers. The system was trained on a dataset of images representing 7 different hand gestures. Testing achieved over 90% accuracy in recognizing the gestures. This vision-based approach allows for natural human-computer interaction without physical devices.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
IRJET- An Efficient VLSI Architecture for 3D-DWT using Lifting SchemeIRJET Journal
This document proposes an efficient VLSI architecture for 3D discrete wavelet transform (DWT) using the lifting scheme. The lifting scheme implementation of DWT has lower area, power consumption and computational complexity compared to convolution-based DWT. The proposed architecture achieves reductions in total area and power compared to existing convolution DWT and discrete cosine transform architectures. It evaluates the performance in terms of area analysis, timing reports, and output matrices after 1D, 2D and 3D DWT using both convolution and lifting schemes. The results show that the lifting scheme provides better compression performance with less area and delay.
This document discusses comparing the performance of different convolutional neural networks (CNNs) when trained on large image datasets using Apache Spark. It summarizes the datasets used - CIFAR-10 and ImageNet - and preprocessing done to standardize image sizes. It then provides an overview of CNN architecture, including convolutional layers, pooling layers, and fully connected layers. Finally, it introduces SparkNet, a framework that allows training deep networks using Spark by wrapping Caffe and providing tools for distributed deep learning on Spark. The goal is to see if SparkNet can provide faster training times compared to a single machine by distributing training across a cluster.
IMAGE CAPTIONING USING TRANSFORMER: VISIONAIDIRJET Journal
The document proposes a new image captioning model called VisionAid that aims to address several issues with existing approaches. It conducts a literature review of transformer-based image captioning methods to identify solutions. VisionAid incorporates grid-level feature extraction, augmented training data diversity using BERT embeddings, and a combination of normalized self-attention and geometric self-attention to better model object relationships while avoiding internal covariate shift issues. The model aims to generate more accurate and diverse captions by leveraging techniques from various transformer models discussed in the literature review.
CAR DAMAGE DETECTION USING DEEP LEARNINGIRJET Journal
This document discusses using deep learning techniques for car damage detection and classification. Specifically, it proposes using a Mask R-CNN model with transfer learning. As there was no publicly available car damage dataset, the authors created their own dataset of 970 images with various damage types labeled. They experimented with different deep learning approaches, finding that transfer learning combined with Mask R-CNN performed best for this task. The proposed methodology involves collecting and labeling images, applying the Mask R-CNN model with transferred features from pre-trained networks, and predicting the damage results.
VIDEO BASED SIGN LANGUAGE RECOGNITION USING CNN-LSTMIRJET Journal
This document presents a proposed method for video-based sign language recognition using convolutional neural networks (CNN) and long short-term memory (LSTM). The method uses CNN to extract spatial features from video frames of sign language and LSTM to analyze the temporal characteristics of the frames to recognize the sign. Color segmentation is used to isolate the hands from video frames by detecting colored gloves worn by the signer. CNN is trained on spatial features from frames to classify signs, and LSTM is used to analyze the sequential features from CNN to recognize signs in full videos. The proposed method achieved 94% accuracy on sign recognition in testing.
Recognition and Detection of Real-Time Objects Using Unified Network of Faste...dbpublications
Region based proposals regularly depend on the features which are economical prudent derivation schemes. The proposed network includesa Region Proposal Network (RPN) which accepts a picture of any size as input and yields an arrangement of rectangular object recommendations, which includes an objectness score. The RPN is prepared end-to-end to produce great quality object recommendations, which are then utilized by Faster R-CNN for object recognition. Further the trained RPN is additionally converged with Faster R-CNN into a solitary system by sharing their convolutional highlights utilizing the as of late famous wording of neural systems with "attention" techniques and the RPN segment advises the brought together system where to look for the object in input. This strategy empowers a unified, profound learning region based proposals for object detection system. The scholarly RPN additionally enhances area proposition quality and accordingly increases the accuracy in object recognition.
Photo Editing And Sharing Web Application With AI- Assisted FeaturesIRJET Journal
The document describes a web application for photo editing and sharing that utilizes generative adversarial networks (GANs) and other machine learning techniques to provide AI-assisted editing features. Specifically, it uses StyleGAN to allow users to semantically edit image attributes like age, pose, and smile without needing expert photo editing skills. The application was developed with Python-Django and its AI features include encoding images into latent spaces, editing the latent vectors to modify attributes, and generating high resolution images. The goal is to make image editing more accessible while producing high fidelity results.
3rd Workshop on Advances in Slicing for Softwarized Infrastructures (S4SI 2020)
Panel: Network Slicing is multifaceted but does its approach and understanding need to be fragmented?
Abstract: Network Slicing keeps growing in significance in the academic and industrial communities. Network Slicing can be defined from different functional or behavioral perspectives, as well as from different viewpoints depending on the stakeholder (e.g., verticals, solution providers, infrastructure owners) and the technical domain (e.g. cloud data centers, radio access, packet/optical transport networks). Standardization bodies and open source projects are being involved in some forms of network slicing support. How far are these views from each other? Is fragmentation leading to incompatible approaches or is there some hope of convergence, at least at conceptual levels? What is the next frontier in Network Slicing? These and other questions will be thrown to our panel experts after introducing their lightning viewpoints.
Moderator: Christian Esteve Rothenberg, University of Campinas, Brazil
Panel Members
Constantine Polychronopoulos, Juniper Networks, USA
Uma Chunduri, Futurewei, USA
Slawomir Kuklinski, Orange Poland and Warsaw University of Technology, Poland
Stuart Clayman, University College London, UK
Augusto Venancio Neto, Federal University of Rio Grande do Norte, Brazil
IRJET- Intelligent Character Recognition of Handwritten CharactersIRJET Journal
This document summarizes research on intelligent character recognition of handwritten characters using neural networks. It discusses how neural networks can be trained on feature vectors extracted from images to accurately recognize (up to 95%) handwritten alphanumeric characters. The proposed system segments images into characters, extracts features like intersections and endpoints, trains a neural network on feature vectors, and then uses the trained network to recognize new characters. It achieved high accuracy after training on a large dataset of 400 samples. The system automatically transfers recognized text to an Excel sheet.
Car Steering Angle Prediction Using Deep LearningIRJET Journal
This document discusses two deep learning models for predicting steering angles of a self-driving car from camera images. The first model uses 3D convolutional layers followed by LSTM layers to incorporate temporal information. The second model uses transfer learning with pre-trained CNN models. Both models were tested in simulation and showed potential for real-world autonomous driving applications by accurately predicting steering angles from camera images alone.
The document proposes combining the Inception neural network architecture with residual connections. It first reviews the Inception and ResNet architectures individually. It then describes new Inception-ResNet models that incorporate residual connections into the Inception blocks. The models are tested on the ImageNet dataset, with an ensemble of Inception-v4 and Inception-ResNet-v2 achieving 3.1% top-5 error, setting a new state-of-the-art. Detailed architectures are provided for the new Inception-v4 and Inception-ResNet variants.
FACE COUNTING USING OPEN CV & PYTHON FOR ANALYZING UNUSUAL EVENTS IN CROWDSIRJET Journal
The document discusses face counting using OpenCV and Python by analyzing unusual events in crowds. It proposes using the Haar cascade algorithm for face detection and counting. Feature extraction is performed using gray-level co-occurrence matrix (GLCM) to extract texture and edge features. Discriminant analysis is then used to differentiate between samples accurately. The system aims to correctly detect and count faces in images using Python tools like OpenCV for digital image processing tasks and feature extraction algorithms like GLCM and discrete wavelet transform (DWT). It is intended to have good recognition accuracy compared to previous methods.
How can you handle defects? If you are in a factory, production can produce objects with defects. Or values from sensors can tell you over time that some values are not "normal". What can you do as a developer (not a Data Scientist) with .NET o Azure to detect these anomalies? Let's see how in this session.
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.
UNLOCKING HEALTHCARE 4.0: NAVIGATING CRITICAL SUCCESS FACTORS FOR EFFECTIVE I...amsjournal
The Fourth Industrial Revolution is transforming industries, including healthcare, by integrating digital,
physical, and biological technologies. This study examines the integration of 4.0 technologies into
healthcare, identifying success factors and challenges through interviews with 70 stakeholders from 33
countries. Healthcare is evolving significantly, with varied objectives across nations aiming to improve
population health. The study explores stakeholders' perceptions on critical success factors, identifying
challenges such as insufficiently trained personnel, organizational silos, and structural barriers to data
exchange. Facilitators for integration include cost reduction initiatives and interoperability policies.
Technologies like IoT, Big Data, AI, Machine Learning, and robotics enhance diagnostics, treatment
precision, and real-time monitoring, reducing errors and optimizing resource utilization. Automation
improves employee satisfaction and patient care, while Blockchain and telemedicine drive cost reductions.
Successful integration requires skilled professionals and supportive policies, promising efficient resource
use, lower error rates, and accelerated processes, leading to optimized global healthcare outcomes.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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.
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
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
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