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Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
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This document discusses using convolutional neural networks (CNNs) to automatically detect diabetic retinopathy from fundus images. It aims to classify images into multiple severity levels, including early stages of the disease. The authors train and test CNN models on two datasets containing over 35,000 retinal images total. While CNNs achieve high accuracy for binary classification, performance decreases with additional severity classes, particularly for mild or early-stage disease. The authors explore techniques like data augmentation and transfer learning to improve CNN performance on multi-class classification of diabetic retinopathy severity levels from fundus images.
This document describes an automated waste management system that uses a convolutional neural network (CNN) algorithm to classify and segregate household waste with minimal human intervention. The system identifies waste as either biodegradable or non-biodegradable and sorts it into separate bins in real time. This helps reduce the human effort and costs required for waste sorting. The system is trained on a dataset of waste images and uses CNNs to learn the features of different waste types and accurately classify new images. Literature on previous waste sorting systems is reviewed, finding CNN approaches can achieve over 90% accuracy on various waste datasets.
Automation of DMPS Manufacturing by using LabView & PLCIJAAS Team
This Paper is to enable the Siemens (Programmable Logic Control) CPU 313- 5A to communicate with the Lab VIEW and to control the process accuracy by image processing. The communication between CPU 313-5A and Lab VIEW is via OPC (OLE for Process Control).Process Accuracy is achieved with the use of Labview Image Processing and Gray Scale matching Pattern. Accuracy in the gray scale matching will purely depend on the calibration of the camera with respect to the corresponding image. The digital output from the labview is communicated to PLC via Ethernet Protocol for the industrial process control. With the use of Labview the dead time while using the normal image vision module in PLC can be minimized. Labview uses the gray scale matching technique whi
DNA microarray technique enables one to analyze the expression of many genes in a single reaction quickly and in an efficient manner. This technique has been elaborately described in this presentation
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Automation of DMPS Manufacturing by Using LabView and PLC IJECEIAES
This Paper is to enable the Siemens (Programmable Logic Control) CPU 313-5A to communicate with the Lab VIEW and to control the process accuracy by image processing. The communication between CPU 313-5A and Lab VIEW is via OPC (OLE for Process Control). Process Accuracy is achieved with the use of Labview Image Processing and Gray Scale matching Pattern. Accuracy in the gray scale matching will purely depend on the calibration of the camera with respect to the corresponding image. The digital output from the labview is communicated to PLC via Ethernet Protocol for the industrial process control. With the use of Labview the dead time while using the normal image vision module in PLC can be minimized. Labview uses the gray scale matching technique which is more accurate than the normal image vision module used in PLC.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
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IRJET- Automated Detection of Diabetic Retinopathy using Deep LearningIRJET Journal
This document discusses using convolutional neural networks (CNNs) to automatically detect diabetic retinopathy from fundus images. It aims to classify images into multiple severity levels, including early stages of the disease. The authors train and test CNN models on two datasets containing over 35,000 retinal images total. While CNNs achieve high accuracy for binary classification, performance decreases with additional severity classes, particularly for mild or early-stage disease. The authors explore techniques like data augmentation and transfer learning to improve CNN performance on multi-class classification of diabetic retinopathy severity levels from fundus images.
This document discusses the development of firefighting drones. It proposes attaching fire extinguisher balls and cameras with heat/temperature sensors to drones to help control the spread of fires. The drones could quickly be sent to fire-affected areas and also be used for surveillance. An alert would be sent to authorities informing them about detected fires using IOT. The document describes the hardware used to build the drones, including propellers, motors, ESC, flight controller and frame. It outlines the planned work and provides references for further research on drone applications.
Covid Detection Using Lung X-ray ImagesIRJET Journal
This document describes a study that used a deep learning model to detect COVID-19 in lung x-ray images. The researchers trained a VGG-16 convolutional neural network on a dataset of over 5,800 x-ray images of both COVID-19 and normal lungs. Data augmentation techniques were used to increase the size and variation of the training dataset. The model achieved 94% accuracy in distinguishing between COVID-19 and normal x-rays. This accurate and fast COVID-19 detection using deep learning could help reduce costs and diagnostic times compared to traditional testing methods.
Review of Classification algorithms for Brain MRI imagesIRJET Journal
1) The document reviews various classification algorithms that have been used to classify brain MRI images as normal or abnormal. It discusses techniques like decision trees, neural networks, fuzzy logic, and clustering that have been applied.
2) It provides examples of several studies that first performed preprocessing tasks like feature extraction on MRI images before applying classification algorithms like naive Bayes, decision trees, and probabilistic neural networks to classify images with accuracies ranging from 88% to 100%.
3) Boosting and ensemble techniques like combining multiple weak learners into a strong learner are mentioned as ways to improve classification accuracy and response times. The document concludes by surveying different algorithms and their performance on classifying brain tumor MRI images.
1. The document discusses using machine learning techniques like deep learning algorithms and convolutional neural networks to detect diabetic retinopathy in retinal images.
2. It proposes using the MobileNetV2 architecture with SVM classification on the APTOS 2019 dataset to classify retinal images as normal or abnormal.
3. The results obtained after applying SVM classification to the APTOS 2019 dataset using MobileNetV2 showed 87% accuracy and a quadratic weighted kappa score of 0.937, indicating the model can accurately detect diabetic retinopathy.
PROFILE DOSE ANALYSIS OF 6MV LINEAR ACCELERATOR WITH CCD ELECTRONIC PORTAL IM...AM Publications
Profile dose analysis of 6 MV linear accelerator use CCD Electronic portal imaging device has been ivestigated. The aim of that research is analysis the profile dose curve of CCD EPID. The analysis include calculate the linierity. Symetrisity and penumbra value. Linier accelerator electa compac and CCD EPID are the material of that research. CCD EPID beamed with 10 x 10 cm field with 5 kind of MU. The MU values are 20 MU until 100 MU. The image of CCD EPID converted to grey-scale. Than we calculated the grey scale value become profile dose curve in cross-line and inline position. The result are we get simetrisity and penumbra less than 2%, but linierity value more 0,2% more than 3%. It means that the symetrisity and penumbra agree with AAPM TG no. 47. But the linerity must has more investigated to decrease he value until 3%.
This document discusses night vision technology. It begins with an introduction and overview of the types and generations of night vision devices. It then discusses the key technologies used in night vision, including image intensification and thermal imaging. It provides examples of different generations of night vision goggles and their performance capabilities. It also discusses applications of night vision technology and some implementations in areas like weapons scopes, surveillance cameras, and goggles. It concludes with challenges of current systems and trends in developing more advanced night vision capabilities.
CROP PROTECTION AGAINST BIRDS USING DEEP LEARNING AND IOTIRJET Journal
1) The document discusses developing a system to detect birds in high-definition video to help protect crops from damage by birds.
2) It explores using convolutional neural networks and background subtraction techniques to identify and classify birds.
3) The methodology section describes taking video input, preprocessing frames, performing background subtraction using mixtures of Gaussians modeling, and evaluating the system's performance using a confusion matrix.
Face recognition using gaussian mixture model & artificial neural networkeSAT Journals
Abstract
Face recognition is a non-contact and friendly biometric identification technology. It has broad application prospects in the
military, public security and economic security. In this work, we also consider illumination variable database. The images have
taken from far distance and do not consider the close view face of the individual as in most of the face databases, clear face view
has been considered. In this first we located face as region of interest and then LBP and LPQ descriptors are used which is
illuminance invariant in nature. After this GMM has been used to reduce feature set by taking negative log-likelihood from each
LBP and LPQ descripted image histograms. After this ANN consumes stayed used for organization purposes. The investigational
consequencesshow excellent correctness rates in overall testing of input data.
Keywords: Illumination invariant, face recognition, LBP, LPQs,GMM,ANN
Performance Comparison Analysis for Medical Images Using Deep Learning Approa...IRJET Journal
This document discusses and compares several deep learning approaches for analyzing medical images, specifically chest x-rays. It first provides an abstract that outlines comparing existing technologies for analyzing chest x-rays using deep learning. It then reviews literature on models like convolutional neural networks (CNN), fully convolutional networks (FCN), lookup-based convolutional neural networks (LCNN), and deep cascade of convolutional neural networks (DCCNN) that have been applied to tasks like image segmentation, classification, and quality assessment of medical images. The document compares the performance of these models on different medical image datasets based on accuracy metrics.
This document describes a study that developed a CNN model to detect COVID-19 in chest X-ray images. The researchers used a dataset of normal, pneumonia, and COVID-19 chest X-rays to train the CNN model. They extracted features from the images using HOG and CNN methods and combined the features as input to the CNN classifier. The CNN model was able to accurately classify X-ray images as COVID-19 positive or not with modifications like data augmentation and noise removal to improve performance. The study aims to provide an effective method for detecting COVID-19 using readily available X-ray machines to help address testing limitations and reduce burden on healthcare systems.
X-Ray Based Quick Covid-19 Detection Using Raspberry-piIRJET Journal
This document summarizes a study that developed an X-ray-based method for quickly detecting COVID-19 using machine learning on a Raspberry Pi. The researchers collected a dataset of chest X-ray images including images of COVID-19, viral pneumonia, and normal cases. They trained a convolutional neural network model using transfer learning with the Inception V3 architecture to classify images as COVID-19, viral pneumonia, or normal with over 98% accuracy. The goal was to create a faster and more accessible method for COVID-19 detection compared to existing testing methods.
The document reviews various techniques for lie detection beyond polygraphs. It discusses 6 lie detection techniques:
1. An ELM and SLFN machine learning approach using EOG and EEG signals achieves 97% accuracy.
2. A multimodal fusion network combining text, audio, and video achieves 92-96% accuracy.
3. An EEG-based system using DWT and SVM achieves 83% accuracy.
4. A system using facial features and audio features analyzed with a DNN achieves 98.45% accuracy.
5. A system using DIFCW radars and machine learning on respiratory and heartbeat signals achieves 61.5-63.2% accuracy.
The document reviews various techniques for lie detection beyond polygraphs. It discusses 6 techniques: 1) An ELM and SLFN machine learning approach using EOG and EEG signals achieves 97% accuracy. 2) A multimodal fusion network using text, audio, and video achieves 92-96% accuracy. 3) A system using EEG and DWT features with SVM achieves 83% accuracy. 4) A system using facial features and audio with DNN achieves 98.45% accuracy. 5) A system using DIFCW radar and machine learning on respiratory and heartbeat signals achieves 61.5-63.2% accuracy. 6) A multimodal system using EEG, audio, and video with Bi-LSTM achie
Lung Cancer Detection Using Convolutional Neural NetworkIRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to classify lung cancer in CT scans. The CNN model is trained on a dataset of 1018 patient CT scans containing annotations of lung nodules as benign or malignant. The CNN architecture includes convolution layers to extract features, max pooling layers to reduce computations, dropout layers to prevent overfitting, and fully connected layers to classify scans. The model achieves a 65% accuracy on the training set at detecting cancer in new CT scans. The CNN is integrated into a web application to allow doctors to efficiently analyze scans for lung cancer.
CovidAID: COVID-19 Detection using Chest X-Ray ImagesIRJET Journal
This document presents research on using deep learning and convolutional neural networks to detect COVID-19 in chest X-ray images. The researchers conducted 38 experiments using various CNN architectures and transfer learning approaches on datasets containing COVID-19, pneumonia, and normal chest X-rays. Their best performing model was a CNN with minimal layers and no preprocessing, achieving mean ROC AUC scores above 95% for COVID-19 detection. Statistical features extracted from the images did not allow other machine learning models to outperform the CNNs. Overall, the experiments demonstrated the potential for automated COVID-19 detection from chest X-rays using deep learning.
1. The document describes a deep learning model to analyze and classify rice quality using images of rice paddies. Rice paddies are photographed and the images are analyzed by a model trained on custom datasets to classify rice purity levels.
2. A convolutional neural network model is built using TensorFlow to classify rice paddies as pure, impure, or partially impure based on image analysis. The model achieves comparable accuracy to state-of-the-art systems.
3. The model can be used by rice mills to automatically analyze rice purity from images and categorize rice without manual inspection, improving efficiency over traditional methods.
IRJET - Identification of Malarial Parasites using Deep LearningIRJET Journal
This document presents a method for identifying malarial parasites using deep learning. The traditional method of manually examining stained blood slides under a microscope is time-consuming and relies on expert availability. The proposed method uses image processing to automate diagnosis and provide quicker, more accurate results. Images of blood samples are preprocessed, segmented, and features are extracted for classification using deep learning models like convolutional neural networks and support vector machines. This can help detect the presence of malarial parasites in blood more sensitively and accurately than manual examination alone.
IRJET - Object Identification in Steel Container through Thermal Image Pi...IRJET Journal
Thermal images of a steel container containing different objects were captured using a thermal camera. The images were filtered to remove noise and then segmented into clusters based on pixel differences, as different materials have unique thermal signatures. A pixel difference matrix map was calculated and feature vectors were extracted from scatter plots of pixel values. Average feature vector values can be used as a reference standard to identify objects inside steel containers based on their thermal properties.
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
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Thermal images of a steel container containing different objects were captured using a thermal camera. The images were filtered to remove noise and then segmented into clusters based on pixel differences, as different materials have unique thermal signatures. A pixel difference matrix map was calculated and feature vectors were extracted from scatter plots of pixel values. Average feature vector values can be used as a reference standard to identify objects inside steel containers based on their thermal properties.
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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.
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
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.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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.
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.
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%.