This document discusses a proposed system for analyzing food cuisine using image processing and machine learning. The system aims to identify the cuisine of a dish by taking either an image of the food or a list of ingredients as input. It would then output the predicted cuisine, along with relevant recipe and nutritional information. The proposed approach aims to overcome limitations of prior work, such as low accuracy and inability to handle both image and ingredient-based inputs. It will utilize a large, detailed dataset and trained machine learning models like KNN to make highly accurate cuisine predictions. The system is intended to benefit food delivery apps and other services by providing customized recommendations to users based on analyzed cuisine preferences.
RECIPE GENERATION FROM FOOD IMAGES USING DEEP LEARNINGIRJET Journal
The document describes a system that can generate cooking recipes from food images using deep learning techniques. It proposes a novel approach that uses CNN, LSTM, and bidirectional LSTM models to extract embeddings of cooking instructions from food images. The system is able to overcome challenges like varying instruction lengths and multiple food items in an image. It was tested on an Indian cuisine dataset and showed potential for applications in information retrieval and automatic recipe recommendation.
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This document discusses a study that uses machine learning to classify food images and recommend foods for health and diet tracking. The researchers collected 1000 food images from various sources to create a dataset with 12 food classes. A convolutional neural network (CNN) model was trained on the dataset and achieved an average accuracy of 86.33% for classifying images into the correct food category. The model can also provide dietary recommendations to help people, such as diabetics, track their health goals. The researchers conclude that combining food classification with recommendation using deep learning is useful for applications like automatic food labeling and dietary assessment.
This document summarizes several research papers on vision-based food analysis systems. It discusses papers on using deep learning techniques like Mask R-CNN and convolutional neural networks to identify and estimate nutrition from food images. Some key applications discussed are food calorie estimation, generating virtual food images to enhance experience, developing large datasets to aid food recognition, analyzing food trays with multiple items, identifying food waste, and mobile apps for recommending recipes from leftovers or identifying halal foods. The document concludes that reviewing these papers provided knowledge on problems and solutions for developing a vision-based food analysis system and highlighted the importance of this topic.
This document describes a proposed online platform called FoodInDaHud that aims to promote healthy eating habits. It consists of a mobile app that allows users to browse recipes, view detailed nutrition information, and customize recipes based on their dietary preferences. The app also enables users to order customized meals locally and have them delivered. The platform utilizes a recipe browser, nutrition profiler, nutrition query engine, and order/delivery system. It seeks to provide users with recipe-level control over their meals and educate them about making informed dietary choices. The platform could be expanded further with additional features like personalized meal plans, special diet options, and voice recipe creation.
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This document describes a recipe recommendation system that uses machine learning models to suggest ingredient pairs and alternative ingredients. It discusses using a vector space model and Word2Vec model to find highly similar ingredient pairs from different cuisines and recommend substitutes. The models are trained on recipe data scraped from online databases. The system aims to help users innovate new dishes and accommodate allergies. It outlines collecting recipe data, preprocessing it, calculating ingredient similarities, and using the models to suggest pairings and alternatives.
IRJET- A Food Recognition System for Diabetic Patients based on an Optimized ...IRJET Journal
This document presents a food recognition system for diabetic patients based on an optimized bag-of-features model. The system extracts dense local features from food images using scale-invariant feature transform on HSV color space. It then builds a visual vocabulary of 10,000 visual words using k-means clustering and classifies the food descriptions with a linear support vector machine classifier. The optimized system achieved a classification accuracy of around 78% on a dataset of over 5,000 food images belonging to 11 classes, demonstrating the feasibility of using a bag-of-features approach for automated food recognition to help with carbohydrate counting for diabetic patients.
WEB BASED NUTRITION AND DIET ASSISTANCE USING MACHINE LEARNINGIRJET Journal
The document describes a proposed web-based nutrition and diet assistance system using machine learning. The system aims to accurately identify foods from images using a convolutional neural network (CNN) model, calculate the nutritional content of the foods, determine the user's BMI, and provide personalized diet recommendations. The system uses techniques like pre-processing, region proposal networks, feature extraction, and CNNs to classify foods, retrieve nutritional data, and suggest diets tailored for the user's BMI and food choices. The goal is to help users conveniently track their nutrition intake and maintain a balanced diet for better health outcomes.
RECIPE GENERATION FROM FOOD IMAGES USING DEEP LEARNINGIRJET Journal
The document describes a system that can generate cooking recipes from food images using deep learning techniques. It proposes a novel approach that uses CNN, LSTM, and bidirectional LSTM models to extract embeddings of cooking instructions from food images. The system is able to overcome challenges like varying instruction lengths and multiple food items in an image. It was tested on an Indian cuisine dataset and showed potential for applications in information retrieval and automatic recipe recommendation.
Food Classification and Recommendation for Health and Diet Tracking using Mac...IRJET Journal
This document discusses a study that uses machine learning to classify food images and recommend foods for health and diet tracking. The researchers collected 1000 food images from various sources to create a dataset with 12 food classes. A convolutional neural network (CNN) model was trained on the dataset and achieved an average accuracy of 86.33% for classifying images into the correct food category. The model can also provide dietary recommendations to help people, such as diabetics, track their health goals. The researchers conclude that combining food classification with recommendation using deep learning is useful for applications like automatic food labeling and dietary assessment.
This document summarizes several research papers on vision-based food analysis systems. It discusses papers on using deep learning techniques like Mask R-CNN and convolutional neural networks to identify and estimate nutrition from food images. Some key applications discussed are food calorie estimation, generating virtual food images to enhance experience, developing large datasets to aid food recognition, analyzing food trays with multiple items, identifying food waste, and mobile apps for recommending recipes from leftovers or identifying halal foods. The document concludes that reviewing these papers provided knowledge on problems and solutions for developing a vision-based food analysis system and highlighted the importance of this topic.
This document describes a proposed online platform called FoodInDaHud that aims to promote healthy eating habits. It consists of a mobile app that allows users to browse recipes, view detailed nutrition information, and customize recipes based on their dietary preferences. The app also enables users to order customized meals locally and have them delivered. The platform utilizes a recipe browser, nutrition profiler, nutrition query engine, and order/delivery system. It seeks to provide users with recipe-level control over their meals and educate them about making informed dietary choices. The platform could be expanded further with additional features like personalized meal plans, special diet options, and voice recipe creation.
IRJET- Recipe Recommendation System using Machine Learning ModelsIRJET Journal
This document describes a recipe recommendation system that uses machine learning models to suggest ingredient pairs and alternative ingredients. It discusses using a vector space model and Word2Vec model to find highly similar ingredient pairs from different cuisines and recommend substitutes. The models are trained on recipe data scraped from online databases. The system aims to help users innovate new dishes and accommodate allergies. It outlines collecting recipe data, preprocessing it, calculating ingredient similarities, and using the models to suggest pairings and alternatives.
IRJET- A Food Recognition System for Diabetic Patients based on an Optimized ...IRJET Journal
This document presents a food recognition system for diabetic patients based on an optimized bag-of-features model. The system extracts dense local features from food images using scale-invariant feature transform on HSV color space. It then builds a visual vocabulary of 10,000 visual words using k-means clustering and classifies the food descriptions with a linear support vector machine classifier. The optimized system achieved a classification accuracy of around 78% on a dataset of over 5,000 food images belonging to 11 classes, demonstrating the feasibility of using a bag-of-features approach for automated food recognition to help with carbohydrate counting for diabetic patients.
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The document describes a proposed web-based nutrition and diet assistance system using machine learning. The system aims to accurately identify foods from images using a convolutional neural network (CNN) model, calculate the nutritional content of the foods, determine the user's BMI, and provide personalized diet recommendations. The system uses techniques like pre-processing, region proposal networks, feature extraction, and CNNs to classify foods, retrieve nutritional data, and suggest diets tailored for the user's BMI and food choices. The goal is to help users conveniently track their nutrition intake and maintain a balanced diet for better health outcomes.
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Quality Analysis and Classification of Rice Grains using Image Processing Tec...IRJET Journal
This document presents a study that aims to analyze and classify rice grains using image processing techniques. The study develops image processing algorithms to segment and identify rice grains in images. By measuring the size (length and breadth) of rice grains through edge detection and a caliper, the algorithms can efficiently analyze grain quality and classify grains. The algorithms are able to generalize classifications across diverse rice varieties by also extracting Fourier features from grain images. The study proposes a methodology involving image pre-processing, morphological operations, edge detection, measurement, and classification to accurately quantify and categorize rice seeds based on size and shape attributes analyzed through images. The methodology aims to provide an alternative approach to rice quality analysis that is more efficient, cost-effective and reduces
This document presents a framework for automatically generating cooking recipes from food images. The framework uses neural networks to recognize ingredients in the food image and then generates step-by-step cooking instructions by considering both the image and predicted ingredients simultaneously. The framework is trained and evaluated on a large dataset of over 1 million recipes. Evaluation shows the framework can improve recipe prediction performance over past approaches and generate convincing and accurate recipes by weighing both the food image and ingredients.
IRJET- Discovery of Recipes based on Ingredients using Machine LearningIRJET Journal
This document describes a machine learning approach to recommend recipes based on available ingredients. It involves scraping recipe data from websites, preprocessing the data, and using a k-nearest neighbors algorithm to classify recipes and recommend matches based on user-inputted ingredients. The system architecture includes data scraping, preprocessing, a database to store recipes and ingredients, and a classification model and user interface to provide recommendations. The goal is to help users more easily find recipes they can make with ingredients they have on hand.
This document presents a framework for generating recipes from food images. The framework first predicts a list of ingredients present in an image using neural networks. It then generates cooking instructions by considering both the image features and predicted ingredients. The system is evaluated on a large dataset and is shown to outperform previous baselines by accurately predicting ingredients and generating coherent recipes directly from images. Key aspects of the framework include using transformers to generate sequential instructions conditioned on image and ingredient embeddings.
Sehat Co. - A Smart Food Recommendation SystemIRJET Journal
This document describes a proposed food recommendation system called Sehat Co. that uses image processing and deep learning techniques to predict ingredients from uploaded food images. It then matches the predicted ingredients against a database of ingredients known to fight COVID-19 to display relevant recipes, stats on COVID-fighting ingredients, and a recommendation for a second recipe. The system aims to help users track their food intake and choose meals containing ingredients that may increase immunity against COVID-19. It was developed using the Recipe1M dataset containing over 1 million recipes and trained on a multi-task deep learning model to perform ingredient prediction and recipe generation and recommendation.
IRJET- Assessing Food Volume and Nutritious Values from Food Images using...IRJET Journal
This document proposes a food recognition system using smartphone cameras to estimate calorie and nutrient values from photos of food. It involves image segmentation, feature extraction, and classification using a decision tree approach. Specifically, the system takes photos of food before and after eating, segments the images to identify the food type and portion size, then uses nutrient databases and the decision tree to estimate calorie content. The goal is to help with obesity treatment by automatically monitoring patients' daily food intake and calories through analysis of food photos. Key steps include pre-processing images, segmenting into regions, measuring food portions, and classifying calories using decision tree algorithms and nutrient fact tables.
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The document proposes an automatic 3D vision-based dietary inspection system for central kitchen automation. The system uses computer vision techniques to detect and locate meal boxes on a conveyor. It then identifies food ingredients in each meal using color, texture, and depth features. Food categories and quantities are determined and compared to dietary requirements. Experimental results showed inspection accuracy of 80%, efficiency of 1200ms, and food quantity accuracy of 90%. The system is expected to increase meal supply capacity by over 50% and help dieticians by automating dietary inspections.
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This document discusses a proposed system for fruit disease detection and fertilizer recommendation using image processing and convolutional neural networks (CNNs). It begins with an introduction to the importance of detecting fruit diseases early to prevent economic losses. It then reviews several existing related works that use techniques like CNNs, k-nearest neighbors, support vector machines, and image processing methods. The proposed system would capture images using a camera, preprocess the images, train a CNN model on a dataset of diseased and healthy fruit images to classify new images, and provide fertilizer/pesticide recommendations. The system is broken down into modules for the frontend user interface, data collection and preprocessing, model building using CNNs, and a backend for analysis and recommendations.
In this paper, we propose an easy approach of
identification and classification of high calorie snacks for dietary
assessment using machine learning. As an object detection
technique we have use point features matching algorithm to
identify the object of interest from a cluttered scene. After
detecting the object, a Bag of Features (BoF) model is created by
extracting Speed up Robust features (SURF) features. This BoF
model is used to recognize and classify the snacks items of different
categories. We have used three types of snacks images named: Icecream,
Chips and Chocolate for experimental purpose. Depending
on the experimental results our proposed algorithm is able to
detect and classify different types of snacks with around 85%
accuracy.
This document describes a proposed AI-based crop identification webapp. The system would use a convolutional neural network (CNN) to identify crop species from images. Users could upload photos of farm yields through a mobile app. The CNN model would be trained on a dataset of labeled plant images. Key aspects of the proposed system include:
1. A training module to develop the CNN model using labeled example images.
2. A testing module to evaluate the trained model's accuracy at identifying crops.
3. An output module allowing users to upload single images for prediction by the CNN model.
The system aims to help farmers identify crops more easily through an automated image recognition system, improving yields and farm management. Experimental results
Automatic meal inspection system using lbp hf feature for central kitchensipij
This paper proposes an intelligent and automatic meal inspection system which can be applied to the meal
inspection for the application of central kitchen automation. The diet specifically designed for the patients are required with providing personalized diet such as low sodium intake or some necessary food. Hence,
the proposed system can benefit the inspection process that is often performed manually. In the proposed
system, firstly, the meal box can be detected and located automatically with the vision-based method and
then all the food ingredients can be identified by using the color and LBP-HF texture features. Secondly,
the quantity for each of food ingredient is estimated by using the image depth information. The experimental results show that the meal inspection accuracy can approach 80%, meal inspection efficiency can reach1200ms, and the food quantity accuracy is about 90%. The proposed system is expected to increase the capacity of meal supply over 50% and be helpful to the dietician in the hospital for saving the time in the diet inspection process.
Potato leaf disease detection using convolutional neural networksIRJET Journal
This document describes a study that used convolutional neural networks to detect three types of potato leaf diseases from images - late blight, early blight, and healthy leaves. The researchers trained a CNN model on a dataset of 1500 labeled potato leaf images. They performed data augmentation and used techniques like image resizing, normalization, and random transformations to improve the model's accuracy. The trained model achieved high performance in identifying the three disease classes, as shown by metrics like accuracy, precision, recall and F1-score. The researchers concluded the CNN model can accurately detect potato leaf diseases and help farmers implement targeted interventions to improve crop health and yields.
IRJET- An Efficient System to Detect Freshness and Quality of FoodIRJET Journal
The document describes a proposed system to detect the freshness and quality of food using sensors. The system would use a hydrogen sensor, moisture sensor, and gas sensor attached to an Arduino board to measure parameters of foods like dairy products and fruits. The sensor readings would be sent to a server for analysis and compared to thresholds to determine if the food is fresh. Results would be displayed on an LCD screen as well as stored in a database. A web application would allow admin, students, and organizations to view the results and submit complaints if the system provides inaccurate results. The goal is to help ensure food safety and reduce food poisoning.
IRJET - An Efficient System to Detect Freshness and Quality of FoodIRJET Journal
This document describes a proposed system to detect the freshness and quality of food using sensors. The system would use a hydrogen sensor, moisture sensor, and gas sensor attached to an Arduino board to measure parameters like pH, moisture content, and ethanol/gas levels in foods. The sensor data would be sent wirelessly to a server and compared to thresholds to determine if the food is fresh or spoiled. Results would be displayed on an LCD screen and stored in a database. A web application would allow administrators, students, and hostel organizations to view the results and submit complaints if the system provides inaccurate readings. The goal is to help ensure food safety and quality by detecting spoilage early before visual signs appear.
1. The document presents a food recommendation system that aims to suggest optimal meals based on a user's available ingredients and tastes.
2. It proposes using data mining techniques like machine learning to analyze patterns in user data and relationships between foods to enhance the system's recommendations.
3. The system is intended to help users decide what to cook or eat by providing personalized recommendations based on their inventory and preferences, taking into account cultural contexts like Indian festivals and recipes.
IRJET- Data Modelling for Database Design in Production and Health Monito...IRJET Journal
This document summarizes a system for monitoring the health of dairy cattle herds using data modeling and database design. Key aspects of the system include:
- Collecting real-time health and production data from dairy herds and storing it in a central SQL database.
- Using clustering and weighting algorithms like Naive Bayes on the stored data to calculate probability values for different health conditions.
- Allowing users to input attributes of individual cows and predict likely health conditions by comparing to the probability values.
- The predictions provide insights to help farmers monitor herd health and take preventive measures to address common issues like mastitis and milk fever.
RECCOMENDATION OF FOOD BASED ON YOUR CURRENT MOODIRJET Journal
The document describes a proposed web application called MoodieFoodie that provides food recommendations tailored to a user's current mood. It aims to address limitations of existing food delivery apps by 1) providing personalized recommendations based on the user's emotional state using machine learning algorithms, 2) simplifying restaurant selection by considering cost, ratings, and distance, and 3) offering a diverse selection of food and beverage options while supporting small businesses. The proposed system is designed to enhance the user experience of food ordering and delivery by connecting emotions and food preferences.
IRJET- Food(Fruit) Quality Recognition by External Appearance and Interna...IRJET Journal
This document summarizes a research paper that proposes a smart fruit grading system using computer vision and sensors to classify fruits by external appearance and internal flavor factors. The system uses a camera to capture images of fruits on a rotating desk and analyzes the images using MATLAB to detect external defects and measure features. Gas sensors are also used to estimate internal quality factors. An artificial neural network model is suggested to classify fruits based on these external and internal criteria. The goal is to develop an automated system that can grade fruits more efficiently and cost-effectively than manual labor.
This document presents a diet recommendation system called MyDietDiary. The system uses machine learning algorithms like k-means and random forest to analyze a user's nutritional intake and recommend a customized diet plan. It collects data on users' health, lifestyle, and food choices to track their body mass index and recommend appropriate meals. The system works by clustering food items based on their nutritional values, classifying foods into meals, and generating diet recommendations tailored to each user's goals and preferences. It allows users to select recommended food items and tracks their daily calorie and macronutrient intake on a dashboard for monitoring their progress. The system aims to help users improve their health and diet through individualized recommendations based on analyzing their profile and
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
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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.
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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 proposes a food recognition system using smartphone cameras to estimate calorie and nutrient values from photos of food. It involves image segmentation, feature extraction, and classification using a decision tree approach. Specifically, the system takes photos of food before and after eating, segments the images to identify the food type and portion size, then uses nutrient databases and the decision tree to estimate calorie content. The goal is to help with obesity treatment by automatically monitoring patients' daily food intake and calories through analysis of food photos. Key steps include pre-processing images, segmenting into regions, measuring food portions, and classifying calories using decision tree algorithms and nutrient fact tables.
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In this paper, we propose an easy approach of
identification and classification of high calorie snacks for dietary
assessment using machine learning. As an object detection
technique we have use point features matching algorithm to
identify the object of interest from a cluttered scene. After
detecting the object, a Bag of Features (BoF) model is created by
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categories. We have used three types of snacks images named: Icecream,
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on the experimental results our proposed algorithm is able to
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accuracy.
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2. A testing module to evaluate the trained model's accuracy at identifying crops.
3. An output module allowing users to upload single images for prediction by the CNN model.
The system aims to help farmers identify crops more easily through an automated image recognition system, improving yields and farm management. Experimental results
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inspection for the application of central kitchen automation. The diet specifically designed for the patients are required with providing personalized diet such as low sodium intake or some necessary food. Hence,
the proposed system can benefit the inspection process that is often performed manually. In the proposed
system, firstly, the meal box can be detected and located automatically with the vision-based method and
then all the food ingredients can be identified by using the color and LBP-HF texture features. Secondly,
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1. The document presents a food recommendation system that aims to suggest optimal meals based on a user's available ingredients and tastes.
2. It proposes using data mining techniques like machine learning to analyze patterns in user data and relationships between foods to enhance the system's recommendations.
3. The system is intended to help users decide what to cook or eat by providing personalized recommendations based on their inventory and preferences, taking into account cultural contexts like Indian festivals and recipes.
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- Collecting real-time health and production data from dairy herds and storing it in a central SQL database.
- Using clustering and weighting algorithms like Naive Bayes on the stored data to calculate probability values for different health conditions.
- Allowing users to input attributes of individual cows and predict likely health conditions by comparing to the probability values.
- The predictions provide insights to help farmers monitor herd health and take preventive measures to address common issues like mastitis and milk fever.
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This document presents a diet recommendation system called MyDietDiary. The system uses machine learning algorithms like k-means and random forest to analyze a user's nutritional intake and recommend a customized diet plan. It collects data on users' health, lifestyle, and food choices to track their body mass index and recommend appropriate meals. The system works by clustering food items based on their nutritional values, classifying foods into meals, and generating diet recommendations tailored to each user's goals and preferences. It allows users to select recommended food items and tracks their daily calorie and macronutrient intake on a dashboard for monitoring their progress. The system aims to help users improve their health and diet through individualized recommendations based on analyzing their profile and
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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.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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%.
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.
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
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.
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.