This document summarizes 11 research papers on using artificial intelligence and machine learning techniques for automated eye disease detection and classification. Several papers describe developing convolutional neural network models trained on fundus image datasets to identify diabetic retinopathy and other eye conditions. Other papers note challenges with early detection of eye diseases, accurately classifying specific disorders, and the need for practical automated detection systems. The document identifies gaps in focusing only on certain diseases, and not addressing difficulties integrating AI into clinical practice.
EYE DISEASE IDENTIFICATION USING DEEP LEARNINGIRJET Journal
This document presents a deep learning model for identifying eye diseases from images. The model was trained on datasets of five different eye conditions - conjunctivitis, cataracts, uveitis, bulging eyes, and crossed eyes. The model uses a convolutional neural network architecture with several convolutional and pooling layers. It achieves 96% accuracy on single-eye images and 92.31% accuracy on two-eye images. The authors conclude the model is effective and cost-efficient at classifying common eye diseases and recommending users seek treatment from ophthalmologists when needed.
A Visionary CNN Approach to Squint Eye Detection and Comprehensive TreatmentIRJET Journal
The document presents a research project that develops a pioneering approach for detecting squint eye (strabismus) and developing comprehensive treatment plans. It utilizes convolutional neural networks (CNNs) trained on a diverse dataset to accurately classify different types of squint eye misalignments. The CNN model is integrated into a web application for real-time diagnosis and treatment recommendations. The approach achieves 98% accuracy in classification, demonstrating its effectiveness in enhancing early detection and personalized treatment for improved patient outcomes and quality of life.
Predicting Autism Spectrum Disorder using Supervised Learning AlgorithmsIRJET Journal
This document presents research on predicting autism spectrum disorder (ASD) using supervised machine learning algorithms. The researchers used a dataset of 704 records containing behavioral and demographic information to train and test random forest, AdaBoost, and support vector machine (SVM) models. They achieved the highest accuracy using AdaBoost. The top three algorithms were then evaluated based on accuracy, sensitivity, specificity and precision. Finally, the researchers integrated the random forest model into a web application using Flask to screen users and predict whether they exhibit ASD traits. The application was tested on sample cases to demonstrate it could accurately predict both non-autistic and autistic outcomes.
Autism Spectrum Disorder Using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to help diagnose and manage Autism Spectrum Disorder (ASD). It begins with an abstract that introduces ASD and how machine learning could be applied. It then provides more details in subsequent sections. The proposed system would use machine learning models trained on large datasets to analyze behaviors and potentially aid earlier and more accurate diagnosis of ASD. It highlights machine learning as a tool to assist clinicians, not replace them. The document outlines steps like data collection, model development and deployment that would be part of the system design. It concludes machine learning has potential to improve ASD diagnosis and treatment if used alongside clinical evaluations, but more research is still needed.
This document discusses diabetic retinopathy detection through deep learning techniques. It summarizes previous research that achieved accuracies between 73-92% detecting diabetic retinopathy using convolutional neural networks like VGG16, MobileNetV1, and MobileNetV2. The authors propose using a MobileNet architecture with dense blocks for image classification of diabetic retinopathy. They achieve 96% accuracy, with precision, recall, and F1 scores of 0.95, 0.98, and 0.97 respectively. Early detection of diabetic retinopathy through this approach could help experts treat patients earlier and prevent vision loss.
A new perspective of refractive error calculation with mobile application IJECEIAES
In many situations, not standardized and limited access to eye health care in several regions of Indonesia becomes the main challenge for myopia patients to measure and monitor their current refractive error condition. Many apps were proposed to provide low-cost alternative measurement tools rather than expensive tools such as Phoropter with Snellen chart and Retinoscopy, but still, those apps need an Internet connection and manually complex steps to operate. These conditions make myopia patients reluctant to use this kind of service. In this regard, we propose an intuitive diopter level measurement app based on mobile application setup, which implements the concept of measure the user face to smartphone screen distance for the rapid diopter calculation processes and at the same time provides a low-cost alternative refractive measurement tool. This paper highlights our experiences when developing a mobile application that can help patients with myopia measuring their blur line distances and evaluate their diopter levels independently. We conduct a number of human trials with the device on a controlled environment to demonstrate the ability of the proposed app to measure the diopter level. The experimental results show that the proposed app is quite successful in measuring the diopter level of myopia patients with a relatively small range of calculation errors compared to optometrist measurement results.
IRJET - Prediction of Autistic Spectrum Disorder based on Behavioural Fea...IRJET Journal
This document summarizes a research paper that aims to predict autism spectrum disorder (ASD) based on behavioral features using machine learning. The researchers collected ASD screening data from different age groups to develop and evaluate neural network models for predicting ASD. They achieved up to 90% accuracy in predicting ASD. The researchers concluded that machine learning is a promising approach for ASD prediction but noted limitations like lack of large datasets. They plan to improve the models by collecting more data from various sources.
Various cataract detection methods-A surveyIRJET Journal
This document summarizes various methods for detecting cataracts. It discusses five different cataract detection methods proposed in previous research: 1) a mobile system using texture analysis and k-NN classification, 2) fundus image processing using histogram equalization, 3) a tri-training method that generates three classifiers, 4) analysis of automatic detection of nuclear and cortical cataracts using fundus images, and 5) enhanced texture features to classify cataractous and non-cataractous lenses. The document also reviews literature on diabetic retinopathy detection and classification. It concludes that while challenges remain, recent applications have potential for early cataract detection and classification.
EYE DISEASE IDENTIFICATION USING DEEP LEARNINGIRJET Journal
This document presents a deep learning model for identifying eye diseases from images. The model was trained on datasets of five different eye conditions - conjunctivitis, cataracts, uveitis, bulging eyes, and crossed eyes. The model uses a convolutional neural network architecture with several convolutional and pooling layers. It achieves 96% accuracy on single-eye images and 92.31% accuracy on two-eye images. The authors conclude the model is effective and cost-efficient at classifying common eye diseases and recommending users seek treatment from ophthalmologists when needed.
A Visionary CNN Approach to Squint Eye Detection and Comprehensive TreatmentIRJET Journal
The document presents a research project that develops a pioneering approach for detecting squint eye (strabismus) and developing comprehensive treatment plans. It utilizes convolutional neural networks (CNNs) trained on a diverse dataset to accurately classify different types of squint eye misalignments. The CNN model is integrated into a web application for real-time diagnosis and treatment recommendations. The approach achieves 98% accuracy in classification, demonstrating its effectiveness in enhancing early detection and personalized treatment for improved patient outcomes and quality of life.
Predicting Autism Spectrum Disorder using Supervised Learning AlgorithmsIRJET Journal
This document presents research on predicting autism spectrum disorder (ASD) using supervised machine learning algorithms. The researchers used a dataset of 704 records containing behavioral and demographic information to train and test random forest, AdaBoost, and support vector machine (SVM) models. They achieved the highest accuracy using AdaBoost. The top three algorithms were then evaluated based on accuracy, sensitivity, specificity and precision. Finally, the researchers integrated the random forest model into a web application using Flask to screen users and predict whether they exhibit ASD traits. The application was tested on sample cases to demonstrate it could accurately predict both non-autistic and autistic outcomes.
Autism Spectrum Disorder Using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to help diagnose and manage Autism Spectrum Disorder (ASD). It begins with an abstract that introduces ASD and how machine learning could be applied. It then provides more details in subsequent sections. The proposed system would use machine learning models trained on large datasets to analyze behaviors and potentially aid earlier and more accurate diagnosis of ASD. It highlights machine learning as a tool to assist clinicians, not replace them. The document outlines steps like data collection, model development and deployment that would be part of the system design. It concludes machine learning has potential to improve ASD diagnosis and treatment if used alongside clinical evaluations, but more research is still needed.
This document discusses diabetic retinopathy detection through deep learning techniques. It summarizes previous research that achieved accuracies between 73-92% detecting diabetic retinopathy using convolutional neural networks like VGG16, MobileNetV1, and MobileNetV2. The authors propose using a MobileNet architecture with dense blocks for image classification of diabetic retinopathy. They achieve 96% accuracy, with precision, recall, and F1 scores of 0.95, 0.98, and 0.97 respectively. Early detection of diabetic retinopathy through this approach could help experts treat patients earlier and prevent vision loss.
A new perspective of refractive error calculation with mobile application IJECEIAES
In many situations, not standardized and limited access to eye health care in several regions of Indonesia becomes the main challenge for myopia patients to measure and monitor their current refractive error condition. Many apps were proposed to provide low-cost alternative measurement tools rather than expensive tools such as Phoropter with Snellen chart and Retinoscopy, but still, those apps need an Internet connection and manually complex steps to operate. These conditions make myopia patients reluctant to use this kind of service. In this regard, we propose an intuitive diopter level measurement app based on mobile application setup, which implements the concept of measure the user face to smartphone screen distance for the rapid diopter calculation processes and at the same time provides a low-cost alternative refractive measurement tool. This paper highlights our experiences when developing a mobile application that can help patients with myopia measuring their blur line distances and evaluate their diopter levels independently. We conduct a number of human trials with the device on a controlled environment to demonstrate the ability of the proposed app to measure the diopter level. The experimental results show that the proposed app is quite successful in measuring the diopter level of myopia patients with a relatively small range of calculation errors compared to optometrist measurement results.
IRJET - Prediction of Autistic Spectrum Disorder based on Behavioural Fea...IRJET Journal
This document summarizes a research paper that aims to predict autism spectrum disorder (ASD) based on behavioral features using machine learning. The researchers collected ASD screening data from different age groups to develop and evaluate neural network models for predicting ASD. They achieved up to 90% accuracy in predicting ASD. The researchers concluded that machine learning is a promising approach for ASD prediction but noted limitations like lack of large datasets. They plan to improve the models by collecting more data from various sources.
Various cataract detection methods-A surveyIRJET Journal
This document summarizes various methods for detecting cataracts. It discusses five different cataract detection methods proposed in previous research: 1) a mobile system using texture analysis and k-NN classification, 2) fundus image processing using histogram equalization, 3) a tri-training method that generates three classifiers, 4) analysis of automatic detection of nuclear and cortical cataracts using fundus images, and 5) enhanced texture features to classify cataractous and non-cataractous lenses. The document also reviews literature on diabetic retinopathy detection and classification. It concludes that while challenges remain, recent applications have potential for early cataract detection and classification.
IRJET- Oral Cancer Detection using Machine LearningIRJET Journal
This paper proposes a machine learning approach to detect oral cancer at early stages. The researchers developed a health application that uses data mining techniques like association rule mining and the Apriori algorithm to analyze datasets of patient attributes and symptoms. The application aims to predict whether a patient has oral cancer based on their input data and classify cases using rules generated by Apriori. It seeks to automate oral cancer prediction and discover relationships between cancer attributes to help clinical decision making.
IRJET- A System for Complete Healthcare Management: Ask-Us-Health A Secon...IRJET Journal
This document proposes a system called ASK-US-HEALTH that uses machine learning algorithms and data mining to provide healthcare management. It aims to help patients access a second medical opinion by entering symptoms and receiving the probable diagnosis. It would also provide doctor recommendations and store patient medical histories and prescriptions. The system intends to improve healthcare access and help manage patient care and data for research through connecting patients, doctors, and nearby pharmacies via a web application.
RETINAL IMAGE CLASSIFICATION USING NEURAL NETWORK BASED ON A CNN METHODSIRJET Journal
This document discusses using a convolutional neural network to classify retinal images. Specifically, it aims to develop a system to distinguish between different retinal diseases using fundus images. The system would extract retinal features from the images like the retina, optic nerve and lesions. It then uses a CNN to detect multiple retinal diseases in fundus photographs from a structured analysis database. The CNN is trained on publicly available retinal image datasets. Neural networks have been found to effectively capture disease-specific color and texture features to enable automated diagnosis similar to human experts. The document also provides background on related work using deep learning and CNNs for tasks like lesion detection and classification of retinal diseases from fundus images.
Faro An Interactive Interface For Remote Administration Of Clinical Tests Bas...Kalle
A challenging goal today is the use of computer networking and advanced
monitoring technologies to extend human intellectual capabilities in medical decision making. Modern commercial eye trackers
are used in many of research fields, but the improvement of eye tracking technology, in terms of precision on the eye movements capture, has led to consider the eye tracker as a tool for vision analysis, so that its application in medical research, e.g. in ophthalmology, cognitive psychology and in neuroscience has grown considerably. The improvements of the human eye tracker interface become more and more important to allow medical doctors to increase their diagnosis capacity, especially if the interface allows them to remotely administer the clinical tests more appropriate for the problem at hand. In this paper, we propose a client/server eye tracking system that provides an interactive system for monitoring patients eye movements depending on the clinical test administered by the medical doctors. The system supports the retrieval of the gaze information and provides statistics to both medical research and disease diagnosis.
IRJET- Detect Malnutrition in Underage Children by using Tensorflow Algor...IRJET Journal
This document describes a system to detect malnutrition in underage children using image processing and machine learning algorithms like TensorFlow. The proposed system analyzes images of children and extracts features to compare with a training dataset in order to diagnose potential malnutrition cases. It aims to help identify malnutrition early without requiring visits to the doctor. The system is intended to benefit those in poverty who have limited access to healthcare. It analyzes color features from input images and matches them to a training set to determine the nutritional status of the subject.
DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING TECHNIQUEIRJET Journal
1) The document discusses a method for detecting diabetic retinal disease using integrated shallow convolutional neural networks, which can improve classification accuracy by 3% on small datasets compared to other CNN techniques.
2) It aims to classify retinal images to detect diabetic retinopathy through shallow CNNs, focusing on cases with limited labelled training data, as deep CNNs typically require large datasets for high accuracy.
3) Experimental results show the proposed approach reduces time cost to around 30% of the smallest dataset tested, which is 10% of the original dataset, while maintaining classification accuracy compared to other integrated CNN learning algorithms.
Predictions And Analytics In Healthcare: Advancements In Machine LearningIRJET Journal
This document discusses advancements in machine learning and predictive analytics for healthcare. It begins with an introduction discussing how technologies like machine learning and artificial intelligence can help researchers and doctors achieve goals faster when integrated with healthcare. The document then reviews literature on challenges with analyzing big healthcare data due to issues like data variety, speed and volume. It discusses different machine learning algorithms that have been used for disease prediction and diagnosis, including decision trees, random forests, bagging and boosting. The methodology section outlines the use of an ensemble approach, combining multiple models to improve overall accuracy. Technologies implemented in this work include Python libraries like Pandas, NumPy and Scikit-learn for data processing and modeling, along with Flask and AWS for web app deployment. The
An automated severity classification model for diabetic retinopathyIRJET Journal
This document presents a study on developing an automated severity classification model for diabetic retinopathy using deep learning techniques. The proposed model uses a modified DenseNet169 architecture with a Convolutional Block Attention Module to classify retinal images into different severity categories of diabetic retinopathy. The model was trained on the Kaggle Asia Pacific Tele-Ophthalmology Society dataset and achieved state-of-the-art performance, accurately classifying 82% of images for severity grading. The lightweight model requires less time and complexity compared to other methods, making it suitable for automated diagnosis of diabetic retinopathy severity.
IRJET- Clinical Medical Knowledge Extraction using Crowdsourcing TechniquesIRJET Journal
This document proposes a system to extract medical knowledge from crowdsourced question answering websites using truth discovery techniques. It aims to determine the trustworthiness of answers provided by doctors on these sites. The system performs medical term extraction from user queries and responses using stemming. It then calculates answer trustworthiness based on factors like doctor expertise, ethnicity, and commitment level. The highest trustworthiness responses are selected as best opinions and stored in a medical blog. A chatbot is also developed to predict disease based on user-reported symptoms. The system aims to provide users with reliable medical information and diagnoses from these crowdsourced sites.
Medic - Artificially Intelligent System for Healthcare Services ...IRJET Journal
This document describes an artificially intelligent system called Medic that aims to provide healthcare services using artificial intelligence technologies. Medic uses natural language processing, fuzzy logic, deep learning and a knowledge base to diagnose diseases from patients' descriptions of their symptoms. It can also recommend medical tests and prescriptions. The system architecture includes interfaces for patients and doctors, a central database, and image recognition and decision making modules. Convolutional neural networks are used for image-based disease identification. The goal of Medic is to make healthcare more accessible and affordable by providing services remotely using artificial intelligence.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Rapid detection of diabetic retinopathy in retinal images: a new approach usi...IJECEIAES
The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.
Smart Pill Reminder and Monitoring SystemIRJET Journal
This document describes a proposed smart pill reminder and monitoring system using IoT technology. The system uses an Arduino microcontroller connected to an IR sensor to detect when medication is taken from a pill box. An ESP module and Android app are used to update the caregiver on medication adherence. The proposed system aims to address limitations of existing methods by integrating additional components like the ESP module and buzzer to send alerts and save data in the cloud. This allows caregivers to remotely monitor patients and ensures medication is taken on schedule.
Artificial Intelligence Detects Diabetic Retinopathy In Real TimeaNumak & Company
Experts placed an algorithm in the artificial intelligence (AI) system they have been working on for 4 years for the diagnosis of Diabetic Retinopathy (DR), which damages the eyesight, and made the system usable in real life. According to ophthalmologists, if the use of this system becomes widespread, a great decrease can be observed in the number of visually impaired people around the world.
IRJET- Data Mining Techniques to Predict DiabetesIRJET Journal
This document discusses using data mining techniques to predict diabetes. It begins with an introduction to diabetes and what causes high blood sugar. It then discusses how data mining of patient purchase histories can show connections to medication adherence. Various data mining techniques are explored, including decision trees and the Apriori algorithm, to analyze medical data and extract patterns to improve diagnosis and treatment recommendations for patients. The goal is to help doctors and patients choose the most effective and lowest cost treatment options based on analyses of large diabetes datasets.
Investigating Assisting Mental Health Condition using Sentiment Analysis thro...IRJET Journal
This document presents a proposed Android application to assist those with mental health conditions using sentiment analysis through natural language processing (NLP). The application would allow users to sign up, take personality tests, track their mental state by writing daily thoughts in a diary with machine learning used to predict mood, chat with other users and mental health professionals, and have one-on-one video conversations with professionals. The goal is to provide accessible remote mental health support for all, as seeking treatment remains stigmatized in some societies and remote areas lack facilities. The proposed system uses technologies like NLP, machine learning, chatbots and video calls to analyze language and facilitate communication within the application.
IRJET- Chronic Kidney Disease Prediction based on Naive Bayes TechniqueIRJET Journal
This document discusses using a Naive Bayes technique to predict chronic kidney disease (CKD) based on patient data. It begins by introducing data mining and its applications in healthcare to extract useful information from large datasets. It then reviews literature on using classification algorithms like Naive Bayes for disease detection. Next, it describes the limitations of existing manual CKD prediction systems. The proposed system would automate CKD prediction using a Naive Bayes classifier to help doctors diagnose the disease which affects many worldwide. The methodology involves collecting clinical data, pre-processing it, then applying the Naive Bayes technique to extract patterns and predict CKD.
IRJET - Prediction and Analysis of Multiple Diseases using Machine Learni...IRJET Journal
This document discusses using machine learning techniques to predict and analyze multiple diseases. It presents research using KNN, support vector machine, random forest, and decision tree algorithms applied to a medical database to predict future and previous diseases. The goal is to provide a smart card method for easily and accurately diagnosing disease by storing an individual's full medical record. It reviews related work applying various machine learning classifiers like decision trees, naive Bayes, and logistic regression to diseases such as heart disease, diabetes, and cancer. The conclusion is that machine learning applied to medical data can help predict disease and save time for patients and doctors.
This document discusses the use of artificial intelligence and machine learning techniques for chronic disease detection and management. It provides background on chronic diseases and their impact globally. It then discusses how machine learning algorithms can be used to analyze medical data from electronic health records to predict chronic diseases and suggest treatments. Various studies that have developed models using techniques like decision trees, neural networks, and random forests to detect diseases like cancer, kidney disease and diabetes are summarized. The ability of artificial intelligence to help diagnose chronic diseases earlier and improve healthcare management is also mentioned.
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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This document discusses using a Naive Bayes technique to predict chronic kidney disease (CKD) based on patient data. It begins by introducing data mining and its applications in healthcare to extract useful information from large datasets. It then reviews literature on using classification algorithms like Naive Bayes for disease detection. Next, it describes the limitations of existing manual CKD prediction systems. The proposed system would automate CKD prediction using a Naive Bayes classifier to help doctors diagnose the disease which affects many worldwide. The methodology involves collecting clinical data, pre-processing it, then applying the Naive Bayes technique to extract patterns and predict CKD.
IRJET - Prediction and Analysis of Multiple Diseases using Machine Learni...IRJET Journal
This document discusses using machine learning techniques to predict and analyze multiple diseases. It presents research using KNN, support vector machine, random forest, and decision tree algorithms applied to a medical database to predict future and previous diseases. The goal is to provide a smart card method for easily and accurately diagnosing disease by storing an individual's full medical record. It reviews related work applying various machine learning classifiers like decision trees, naive Bayes, and logistic regression to diseases such as heart disease, diabetes, and cancer. The conclusion is that machine learning applied to medical data can help predict disease and save time for patients and doctors.
This document discusses the use of artificial intelligence and machine learning techniques for chronic disease detection and management. It provides background on chronic diseases and their impact globally. It then discusses how machine learning algorithms can be used to analyze medical data from electronic health records to predict chronic diseases and suggest treatments. Various studies that have developed models using techniques like decision trees, neural networks, and random forests to detect diseases like cancer, kidney disease and diabetes are summarized. The ability of artificial intelligence to help diagnose chronic diseases earlier and improve healthcare management is also mentioned.
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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.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
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.
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.
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.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.