This document describes a system for recognizing plants using leaf images with neural networks and computer vision. The system first collects leaf images and performs image augmentation to increase the training data size. It then pre-processes the images by resizing them, converting to grayscale, and applying edge detection filters. The features extracted from this are used to train a neural network model to classify leaf images into different plant types. The trained model is then able to predict the plant type of new leaf images based on the features it has learned to identify during training. The system was tested on images of 5 different plant species and was able to accurately classify leaf images into the correct plant types.
Detection of medical instruments project- PART 2Sairam Adithya
this presentation is a continuation of the previous one. In this presentation, the work process for individual steps has been clearly explained with snippets of code taken from the source code. This is present along with output visualization, advantages and conclusion.
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
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.
Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from B...IJECEIAES
Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC.
Detection of medical instruments project- PART 2Sairam Adithya
this presentation is a continuation of the previous one. In this presentation, the work process for individual steps has been clearly explained with snippets of code taken from the source code. This is present along with output visualization, advantages and conclusion.
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
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.
Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from B...IJECEIAES
Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC.
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.
Automated Crop Inspection and Pest Control Using Image ProcessingIJERDJOURNAL
ABSTRACT: Agriculture is the backbone of our country. India is an agricultural country where the most of the population depends on agriculture. Research in agriculture is aimed towards increasing productivity and profit. There are several automated systems available in literature, which are developed for irrigation control and environmental monitoring in the field. However, it is essential to monitor the plant growth stage by stage and take decisions accordingly. In addition to monitoring the environmental parameters such as pH, moisture content and temperature, it is inevitable to identify the onset of plant diseases too. It is the key to prevent the losses in yield and quantity of agricultural product. Plant disease identification by continuous visual monitoring is very difficult task to farmers and at the same time it is less accurate and can be done in limited areas. Hence this projects aims at developing an image processing algorithm to identify the diseases in rice plant. Rice blast disease occurring in rice plant is due to magnaporthe grisea and this disease also occurs in wheat, rye, barley, pearl and millet. Due to rice blast disease, 60 million people are affected in 85 countries worldwide. Image processing technique is adopted as it is more accurate. Early disease detection can increase the crop production by inducing proper pesticide usage.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
Rice continues to be a primary food for the world’s population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of
the proposed approach for rice disease detection and treatments.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
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
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Brain tumour segmentation based on local independent projection based classif...eSAT Journals
Abstract
Brain tumour detection and segmentation is most important and challenging task in early tumour diagnosis. There are various
segmentation methods available but they are still challenging methods because of its complex characteristics such as ambiguous
boundaries and high diversity. To overcome this problem we are going to implement automatic brain tumour detection and
segmentation method by using local independent projection based classification. In this method we are going to consider tumour
segmentation as a classification problem. In this paper locality is important in calculations of projections. Also local anchor
embedding is used to solve linear projection weights. The softmax regression model is used to improve classification performance.
In this study we used MRI images as training and testing data. Finally the brain tumour is classified into tumour and edema
region. The area of tumour region is calculated in pixels.
Key Words: Brain tumour detection & segmentation, local independent projection based classification, local anchor
embedding and softmax regression.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
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.
Automated Crop Inspection and Pest Control Using Image ProcessingIJERDJOURNAL
ABSTRACT: Agriculture is the backbone of our country. India is an agricultural country where the most of the population depends on agriculture. Research in agriculture is aimed towards increasing productivity and profit. There are several automated systems available in literature, which are developed for irrigation control and environmental monitoring in the field. However, it is essential to monitor the plant growth stage by stage and take decisions accordingly. In addition to monitoring the environmental parameters such as pH, moisture content and temperature, it is inevitable to identify the onset of plant diseases too. It is the key to prevent the losses in yield and quantity of agricultural product. Plant disease identification by continuous visual monitoring is very difficult task to farmers and at the same time it is less accurate and can be done in limited areas. Hence this projects aims at developing an image processing algorithm to identify the diseases in rice plant. Rice blast disease occurring in rice plant is due to magnaporthe grisea and this disease also occurs in wheat, rye, barley, pearl and millet. Due to rice blast disease, 60 million people are affected in 85 countries worldwide. Image processing technique is adopted as it is more accurate. Early disease detection can increase the crop production by inducing proper pesticide usage.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
Rice continues to be a primary food for the world’s population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of
the proposed approach for rice disease detection and treatments.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
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
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Brain tumour segmentation based on local independent projection based classif...eSAT Journals
Abstract
Brain tumour detection and segmentation is most important and challenging task in early tumour diagnosis. There are various
segmentation methods available but they are still challenging methods because of its complex characteristics such as ambiguous
boundaries and high diversity. To overcome this problem we are going to implement automatic brain tumour detection and
segmentation method by using local independent projection based classification. In this method we are going to consider tumour
segmentation as a classification problem. In this paper locality is important in calculations of projections. Also local anchor
embedding is used to solve linear projection weights. The softmax regression model is used to improve classification performance.
In this study we used MRI images as training and testing data. Finally the brain tumour is classified into tumour and edema
region. The area of tumour region is calculated in pixels.
Key Words: Brain tumour detection & segmentation, local independent projection based classification, local anchor
embedding and softmax regression.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
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.
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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.
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.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.