This document presents a method for automated detection of dengue virus infection using images of white blood cells. The method involves preprocessing images using median filtering to remove noise, segmentation of white blood cells using morphological thresholding, feature extraction of cell nuclei using SIFT, and classification of cells as infected or non-infected using support vector machines (SVM). Testing showed the method achieved higher accuracy than existing techniques, with 75% accuracy compared to 65% for existing methods. The authors also explored using a two-layer feedforward neural network for classification, which achieved even higher accuracy than SVM.
Implementation of Malaria Parasite Detection System Using Image Processingijtsrd
Malaria is a critical disease for which the instant detection is essential so as to control it. Microscopes are used to detect the disease and pathologists use the manual technique because of which there is several chance of incorrect detection being made regarding the disease. If the incorrect detection is made then the disease can turn into more difficult situation. So the study relating to the computerized detection is done in this paper that will facilitate in instant detection of the disease to some level. An image processing scheme is capable to enhance outcome of malaria parasite cell detection. In image processing image consistency is very essential to acquire correct result. Therefore to increase the correctness of the malaria detection system, we proposed new image processing based system which includes two algorithms. One is Haar wavelet algorithm for image transformation and other is K nearest neighbor algorithm for image classification. This system helps to reduce time as well as offer the better accuracy to detect Malaria to some degree. Kanchan N. Poharkar | Dr. S. A. Ladhake"Implementation of Malaria Parasite Detection System Using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12798.pdf http://www.ijtsrd.com/computer-science/other/12798/implementation-of-malaria-parasite-detection-system-using-image-processing/kanchan-n-poharkar
Utilization of Super Pixel Based Microarray Image Segmentationijtsrd
In the division of PC vision pictures, Super pixels are go probably as key part from 10 years prior. There are various counts and methodology to separate the Super pixels anyway whole all of them the best super pixel looking at strategy is Simple Linear Iterative Clustering SLIC have come to pivot continuously recently. The concentrating of small scale group quality verbalization from MRI imaging is more useful to perceive tumors or some other dangerous development contaminations, so the fundamental DNA cDNA microarray is a grounded device for analyzing the same. The division of microarray pictures is the essential development in a microarray assessment. In this paper, we proposed a figuring to dividing the cDNA small show picture using Simple Linear Iterative Clustering SLIC based Self Organizing Maps SOM method. In any case, the proposed figuring is taken up a moving task to look at the bad quality of pictures in addition. There are two phases to separate the image, introductory, a pre setting up the applied picture to diminish fuss levels and second, to piece the image using SLIC based SOM approach. Mr. Davu Manikanta | Mr. Parasurama N | K Keerthi "Utilization of Super Pixel Based Microarray Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46274.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/46274/utilization-of-super-pixel-based-microarray-image-segmentation/mr-davu-manikanta
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Segmentation of unhealthy region of plant leaf using image processing techniqueseSAT Journals
Abstract A segmentation technique is used to segment the diseased portion of a leaf. Based on the segmented area texture and color feature, disease can be identified by classification technique. There are many segmentation techniques such as Edge detection, Thresholding, K-Means clustering, Fuzzy C-Means clustering, Penalized Fuzzy C-Means, Unsupervised segmentation. Segmentation of diseased area of a plant leaf is the first step in disease detection and identification which plays crucial role in agriculture research. This paper provides different segmentation techniques that are used to segment diseased leaf of a plant. Keywords: Fuzzy C-Means, K-Means, Penalized FCM, Unsupervised Fuzzy Clustering
The Fish disease causes several losses in the fish farm. A number of fungal and bacterial diseases
especially EUS (Epizootic Ulcerative Syndrome) causing Morbidity and Mortality in fish. Fish Infection caused
by Aphanomyces invadans is commonly known as EUS (Epizootic Ulcerative Syndrome) and it is due to the
primary fungal pathogen. EUS disease is still misidentified by the people. The paper proposed a combination of
feature extractor with PCA (Principle component analysis) and classifier for better accuracy. Proposed
combination of techniques gives better accuracy while identification of EUS and Non EUS infected fishes.
After experimentation, it is found that PCA improves the performance of classifier after reducing the
dimensions. Real images of EUS infected fishes have been used throughout and all the work is done in
MATLAB.
Implementation of Malaria Parasite Detection System Using Image Processingijtsrd
Malaria is a critical disease for which the instant detection is essential so as to control it. Microscopes are used to detect the disease and pathologists use the manual technique because of which there is several chance of incorrect detection being made regarding the disease. If the incorrect detection is made then the disease can turn into more difficult situation. So the study relating to the computerized detection is done in this paper that will facilitate in instant detection of the disease to some level. An image processing scheme is capable to enhance outcome of malaria parasite cell detection. In image processing image consistency is very essential to acquire correct result. Therefore to increase the correctness of the malaria detection system, we proposed new image processing based system which includes two algorithms. One is Haar wavelet algorithm for image transformation and other is K nearest neighbor algorithm for image classification. This system helps to reduce time as well as offer the better accuracy to detect Malaria to some degree. Kanchan N. Poharkar | Dr. S. A. Ladhake"Implementation of Malaria Parasite Detection System Using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12798.pdf http://www.ijtsrd.com/computer-science/other/12798/implementation-of-malaria-parasite-detection-system-using-image-processing/kanchan-n-poharkar
Utilization of Super Pixel Based Microarray Image Segmentationijtsrd
In the division of PC vision pictures, Super pixels are go probably as key part from 10 years prior. There are various counts and methodology to separate the Super pixels anyway whole all of them the best super pixel looking at strategy is Simple Linear Iterative Clustering SLIC have come to pivot continuously recently. The concentrating of small scale group quality verbalization from MRI imaging is more useful to perceive tumors or some other dangerous development contaminations, so the fundamental DNA cDNA microarray is a grounded device for analyzing the same. The division of microarray pictures is the essential development in a microarray assessment. In this paper, we proposed a figuring to dividing the cDNA small show picture using Simple Linear Iterative Clustering SLIC based Self Organizing Maps SOM method. In any case, the proposed figuring is taken up a moving task to look at the bad quality of pictures in addition. There are two phases to separate the image, introductory, a pre setting up the applied picture to diminish fuss levels and second, to piece the image using SLIC based SOM approach. Mr. Davu Manikanta | Mr. Parasurama N | K Keerthi "Utilization of Super Pixel Based Microarray Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46274.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/46274/utilization-of-super-pixel-based-microarray-image-segmentation/mr-davu-manikanta
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Segmentation of unhealthy region of plant leaf using image processing techniqueseSAT Journals
Abstract A segmentation technique is used to segment the diseased portion of a leaf. Based on the segmented area texture and color feature, disease can be identified by classification technique. There are many segmentation techniques such as Edge detection, Thresholding, K-Means clustering, Fuzzy C-Means clustering, Penalized Fuzzy C-Means, Unsupervised segmentation. Segmentation of diseased area of a plant leaf is the first step in disease detection and identification which plays crucial role in agriculture research. This paper provides different segmentation techniques that are used to segment diseased leaf of a plant. Keywords: Fuzzy C-Means, K-Means, Penalized FCM, Unsupervised Fuzzy Clustering
The Fish disease causes several losses in the fish farm. A number of fungal and bacterial diseases
especially EUS (Epizootic Ulcerative Syndrome) causing Morbidity and Mortality in fish. Fish Infection caused
by Aphanomyces invadans is commonly known as EUS (Epizootic Ulcerative Syndrome) and it is due to the
primary fungal pathogen. EUS disease is still misidentified by the people. The paper proposed a combination of
feature extractor with PCA (Principle component analysis) and classifier for better accuracy. Proposed
combination of techniques gives better accuracy while identification of EUS and Non EUS infected fishes.
After experimentation, it is found that PCA improves the performance of classifier after reducing the
dimensions. Real images of EUS infected fishes have been used throughout and all the work is done in
MATLAB.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
In this project, we propose a new novel DNN-based automatic detection of diabetic retinopathy. In deep neural networks are used for classify the images that indicate diabetic retinopathy. The main aim of this project is to find the suitable way to detect the problems and classify them. We propose an deep neural network (RBFNN) classifier gives high precision in grouping of these disease through spatial examination. The RBFNN classifier does not require an large training time, therefore the model production can be expedited. We further find from our data set of 80,000 images used in our proposed RBFNN achieves a sensitivity of 95% and an accuracy of 75% on 5000 validation images. The fuzzy c means clustering is used to store the information as the processed images in this project . Finally, the proposed system is developed using matlab simulation.
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Iris recognition for personal identification using lamstar neural networkijcsit
One of the promising biometric recognition method is Iris recognition. This is because the iris texture provides many features such as freckles, coronas, stripes, furrows, crypts, etc. Those features are unique for different people and distinguishable. Such unique features in the anatomical structure of the iris make it
possible the differentiation among individuals. So during last year’s huge number of people have been
trying to improve its performance. In this article first different common steps for the Iris recognition system
is explained. Then a special type of neural network is used for recognition part. Experimental results show high accuracy can be obtained especially when the primary steps are done well.
Spectral analysis of remotely sensed images provide the required information accurately even for small
targets. Hence Hyperspectral imaging is being used which follows the technique of dividing images into
bands. These Hyperspectral images find their applications in agriculture, biomedical, marine analysis, oil
seeps detection etc. A Hyperspectral image contains many spectra, one for each individual point on the
sample’s surface and in this project the required target on the Hyperspectral image is going to be detected
and classified. Hyperspectral remote sensing image classification is a challenging problem because of its
high dimensional inputs, many class outputs and limited availability of reference data. Therefore some
powerful techniques to improve the accuracy of classification are required. The objective of our project is
to reduce the dimensionality of the Hyperspectral image using Principal Component Analysis followed by
classification using Neural Network. The project is to be implemented using MATLAB.
Disease Detection in Plant Leaves using K-Means Clustering and Neural Networkijtsrd
The most contributing variable for the Indian Economy is Agriculture yet at the same time there is absence of mechanical improvement in many parts of it. The harm caused by rising, re developing and endemic pathogens, is vital in plant frameworks and prompts potential misfortune. The harvest generation misfortunes its quality because of much infections and some of the time they happen however are indeed, even not obvious with stripped eyes. Plant malady recognition is one such dull process that is hard to be inspected by exposed eye. This paper shows an answer utilizing image processing calculations by loading the image, preprocessing and feature extraction using K means clustering and segmentation method to identify the disease with which the plant leaf been affected. P. Harini | V. Chandran "Disease Detection in Plant Leaves using K-Means Clustering and Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29562.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29562/disease-detection-in-plant-leaves-using-k-means-clustering-and-neural-network/p-harini
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
In this project, we propose a new novel DNN-based automatic detection of diabetic retinopathy. In deep neural networks are used for classify the images that indicate diabetic retinopathy. The main aim of this project is to find the suitable way to detect the problems and classify them. We propose an deep neural network (RBFNN) classifier gives high precision in grouping of these disease through spatial examination. The RBFNN classifier does not require an large training time, therefore the model production can be expedited. We further find from our data set of 80,000 images used in our proposed RBFNN achieves a sensitivity of 95% and an accuracy of 75% on 5000 validation images. The fuzzy c means clustering is used to store the information as the processed images in this project . Finally, the proposed system is developed using matlab simulation.
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Iris recognition for personal identification using lamstar neural networkijcsit
One of the promising biometric recognition method is Iris recognition. This is because the iris texture provides many features such as freckles, coronas, stripes, furrows, crypts, etc. Those features are unique for different people and distinguishable. Such unique features in the anatomical structure of the iris make it
possible the differentiation among individuals. So during last year’s huge number of people have been
trying to improve its performance. In this article first different common steps for the Iris recognition system
is explained. Then a special type of neural network is used for recognition part. Experimental results show high accuracy can be obtained especially when the primary steps are done well.
Spectral analysis of remotely sensed images provide the required information accurately even for small
targets. Hence Hyperspectral imaging is being used which follows the technique of dividing images into
bands. These Hyperspectral images find their applications in agriculture, biomedical, marine analysis, oil
seeps detection etc. A Hyperspectral image contains many spectra, one for each individual point on the
sample’s surface and in this project the required target on the Hyperspectral image is going to be detected
and classified. Hyperspectral remote sensing image classification is a challenging problem because of its
high dimensional inputs, many class outputs and limited availability of reference data. Therefore some
powerful techniques to improve the accuracy of classification are required. The objective of our project is
to reduce the dimensionality of the Hyperspectral image using Principal Component Analysis followed by
classification using Neural Network. The project is to be implemented using MATLAB.
Disease Detection in Plant Leaves using K-Means Clustering and Neural Networkijtsrd
The most contributing variable for the Indian Economy is Agriculture yet at the same time there is absence of mechanical improvement in many parts of it. The harm caused by rising, re developing and endemic pathogens, is vital in plant frameworks and prompts potential misfortune. The harvest generation misfortunes its quality because of much infections and some of the time they happen however are indeed, even not obvious with stripped eyes. Plant malady recognition is one such dull process that is hard to be inspected by exposed eye. This paper shows an answer utilizing image processing calculations by loading the image, preprocessing and feature extraction using K means clustering and segmentation method to identify the disease with which the plant leaf been affected. P. Harini | V. Chandran "Disease Detection in Plant Leaves using K-Means Clustering and Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29562.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29562/disease-detection-in-plant-leaves-using-k-means-clustering-and-neural-network/p-harini
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.