Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
Diseases in plants cause major production and economic losses as well as a reduction in both the quality and quantity of agricultural products. In India, 70% of the population depends on agriculture and contributes 17% towards the GDP of the country. Now a day’s plant disease detection has received increasing attention in monitoring large fields of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for the detection and identification of plant diseases.
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to a lack of the necessary infrastructure. The combination of increasing global smartphone penetration and the recent advancement, in computer vision made possible by deep learning, and transfer learning has paved the way for smart systems to diagnose diseases at initial stages, as soon as they appear in plant leaves.
Therefore, a convolutional neural network is created and developed to perform plant disease detection and classification using leaf images of healthy and diseased of 18 crops. Recent developments in deep neural networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. Deep Learning (DL) is the fastest growing and a broader part of the machine learning family. Deep learning uses convolutional neural networks for image classification as it gives the most accurate results in solving real-world problems.
Creating and training a CNN model from scratch is a tedious process when compared to the usage of existing deep learning models for various applications to achieve maximum accuracy. So depending on the application various models can be used or retrained. In this project, we have implemented VGG16 and VGG19 architecture for the leaf diseases of 18 crops and compare their accuracy, VGG16 have shown slightly good accuracy as compared to that of VGG19, using “New Plant Disease Dataset” to train and validate both the models, which contains 87k images of 38 different plant leaf diseases.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
Diseases in plants cause major production and economic losses as well as a reduction in both the quality and quantity of agricultural products. In India, 70% of the population depends on agriculture and contributes 17% towards the GDP of the country. Now a day’s plant disease detection has received increasing attention in monitoring large fields of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for the detection and identification of plant diseases.
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to a lack of the necessary infrastructure. The combination of increasing global smartphone penetration and the recent advancement, in computer vision made possible by deep learning, and transfer learning has paved the way for smart systems to diagnose diseases at initial stages, as soon as they appear in plant leaves.
Therefore, a convolutional neural network is created and developed to perform plant disease detection and classification using leaf images of healthy and diseased of 18 crops. Recent developments in deep neural networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. Deep Learning (DL) is the fastest growing and a broader part of the machine learning family. Deep learning uses convolutional neural networks for image classification as it gives the most accurate results in solving real-world problems.
Creating and training a CNN model from scratch is a tedious process when compared to the usage of existing deep learning models for various applications to achieve maximum accuracy. So depending on the application various models can be used or retrained. In this project, we have implemented VGG16 and VGG19 architecture for the leaf diseases of 18 crops and compare their accuracy, VGG16 have shown slightly good accuracy as compared to that of VGG19, using “New Plant Disease Dataset” to train and validate both the models, which contains 87k images of 38 different plant leaf diseases.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Smart health disease prediction python djangoShaikSalman28
mca final year project of Smart health disease prediction python django ppt. It is also helpful for mtech students also. Can anyone need this project coding then call me 9491831577. if you want extra projects then also u can call me . Smart health disease prediction python django price is 300rs.
Disease prediction and doctor recommendation systemsabafarheen
This paper will tell you how the system will work in terms of disease prediction also will suggest you nearest hospital with experienced doctors, cheap fees
Plant disease detection using machine learning algorithm-1.pptxRummanHajira
Plant disease detection and classification using machine learning algorithm - It gives you a glance of introduction on why do we have to detect and classify the diseases along with the IEEE papers as a reference to the titled project
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Smart health disease prediction python djangoShaikSalman28
mca final year project of Smart health disease prediction python django ppt. It is also helpful for mtech students also. Can anyone need this project coding then call me 9491831577. if you want extra projects then also u can call me . Smart health disease prediction python django price is 300rs.
Disease prediction and doctor recommendation systemsabafarheen
This paper will tell you how the system will work in terms of disease prediction also will suggest you nearest hospital with experienced doctors, cheap fees
Plant disease detection using machine learning algorithm-1.pptxRummanHajira
Plant disease detection and classification using machine learning algorithm - It gives you a glance of introduction on why do we have to detect and classify the diseases along with the IEEE papers as a reference to the titled project
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Fault Detection in Mobile Communication Networks Using Data Mining Techniques...ijcisjournal
A collection of datasets is Big data so that it to be To process huge and complex datasets becomes difficult.
so that using big data analytics the process of applying huge amount of datasets consists of many data
types is the big data on-hand theoretical models and technique tools. The technology of mobile
communication introduced low power ,low price and multi functional devices. A ground for data mining
research is analysis of data pertaining to mobile communication is used. theses mining frequent patterns
and clusters on data streams collaborative filtering and analysis of social network. The data analysis of
mobile communication has been often used as a background application to motivate many technical
problem in data mining research. This paper refers in mobile communication networking to find the fault
nodes between source to destination transmission using data mining techniques and detect the faults using
outliers. outlier detection can be used to find outliers in multivariate data in a simple ensemble way.
Network analysis with R to build a network.
Image Processing and Deep Learning (EEEM063) DIGITS IntrodMalikPinckney86
Image Processing and Deep Learning (EEEM063)
DIGITS Introductory Deep Learning Lab
Dr John Collomosse - Autumn 2017
Introduction
In this lab you will use a web based interface called “DIGITS” to run some simple classification
experiments using a convolution neural network (CNN). This lab assumes use of DIGITS v3.0.0 or
higher.
CNNs are state of the art deep neural networks that perform well at machine perception tasks such
as image classification. You will start learning about these in detail within Week 6.
To do this work you will need to connect to a teaching server
called aineko1 on the campus network. The server is not
visible off campus, so you are encouraged to use the lab or
library PCs on campus to do this work. If you want to use
your own laptop then you will be able to connect via the
campus wide “eduroam” wifi network. The campus wide
“The Cloud” network will not work. It is possible to connect
from offcampus using the https://anywhere.surrey.ac.uk
facility and entering the web interface address including
http:// into the text box in the upper-right of the portal.
The address of the web interface is http://aineko.eps.surrey.ac.uk:34448
Verify now that you can connect to the web interface via your browser, or you will not be able to
progress any further.
It is possible to install your own version of DIGITS on your local lab PC (e.g. in the Penguin lab). Since
we have a large class this year this may be useful if the aineko server becomes very busy. You can
refer to the supplementary instructions on SurreyLearn if you find is desirable to do this. If you want
to install DIGITS yourself on your own machine then you might find those instructions a useful
starting point but note that we do not have the resource to assist 50+ students installing their own
DIGITS on their own variants/configurations of Linux so if you try this you are on your own.
The aineko server (pictured right) is an Intel i7 PC with 4 Nvidia Titan-X GPUs which power our deep
learning experiments.
1. Getting Started
We will be working with a dataset of hand-written numbers 0-9, collected by the US postal service
from mail. The dataset is called MNIST and contains 70k images each only 28x28 pixels in size.
1 https://en.wikipedia.org/wiki/Accelerando
You must first create your own work area on the server and download the database.
Remotely login to the teaching server using ssh. In the Linux labs you can open up a terminal
window using Ctrl-Alt-T and then enter the following
ssh aineko.eps.surrey.ac.uk –l your_username
Note that character after the hyphen is a lower case L not a 1! Please substitute username you’re
your actual username. The password is your URN. If you are prompted by an “are you sure…”
prompt just type the word yes. If you can’t log in the we will have to create you an account.
Alternatively you may issue the same command in the Mac OS “Terminal” (usually found under
/Appl ...
HANDWRITTEN DIGIT RECOGNITION SYSTEM BASED ON CNN AND SVMsipij
The recognition of handwritten digits has aroused the interest of the scientific community and is the subject
of a large number of research works thanks to its various applications. The objective of this paper is to
develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN)
combined with machine learning approaches to ensure diversity in automatic classification tools. In this
work, we propose a classification method based on deep learning, in particular the convolutional neural
network for feature extraction, it is a powerful tool that has had great success in image classification,
followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and
the results obtained showed that the combination of CNN with SVM improves the performance of the model
as well as the classification accuracy with a rate of 99.12%.
Handwritten Digit Recognition System based on CNN and SVMsipij
The recognition of handwritten digits has aroused the interest of the scientific community and is the subject
of a large number of research works thanks to its various applications. The objective of this paper is to
develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN)
combined with machine learning approaches to ensure diversity in automatic classification tools. In this
work, we propose a classification method based on deep learning, in particular the convolutional neural
network for feature extraction, it is a powerful tool that has had great success in image classification,
followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and
the results obtained showed that the combination of CNN with SVM improves the performance of the model
as well as the classification accuracy with a rate of 99.12%.
In the healthcare industry, patients are often exposed to harmful pathogens due to a lack of compliance to hand hygiene protocol. The vast majority of healthcare professionals do not abide by hand hygiene standards established by the World Health Organization, facilitating the spread of nosocomial (hospital-acquired) infections. When representatives trained in proper handwashing procedures monitored medical professionals, there was a significant increase in compliance with proper protocol. Given this correlation between observance and adherence, a Hygiene Monitoring System was developed to monitor handwashing through the application of machine learning. The embedded system captured, processed, and compared instances of handwashing to the proper procedure. An implementation of this system would encourage healthcare professionals to follow the official protocol denoted by the World Health Organization and dramatically reduce the likelihood of healthcare–associated infections.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
DETECTING EMOTION FROM FACIAL EXPRESSION HAS BECOME AN URGENT NEED BECAUSE OF
ITS IMMENSE APPLICATIONS IN ARTIFICIAL INTELLIGENCE SUCH AS HUMAN-COMPUTER
COLLABORATION, DATA DRIVEN ANIMATION, HUMAN-ROBOT COMMUNICATION ETC. SINCE IT
IS A DEMANDING AND INTERESTING PROBLEM IN COMPUTER VISION, SEVERAL WORKS HAD
BEEN CONDUCTED REGARDING THIS TOPIC. THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP A
FACIAL EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
WITH DATA AUGMENTATION. THIS APPROACH ENABLES TO CLASSIFY SEVEN BASIC EMOTIONS
CONSIST OF ANGRY, DISGUST, FEAR, HAPPY, NEUTRAL, SAD AND SURPRISE FROM IMAGE DATA.
CONVOLUTIONAL NEURAL NETWORK WITH DATA AUGMENTATION LEADS TO HIGHER
VALIDATION ACCURACY THAN THE OTHER EXISTING MODELS (WHICH IS 96.24%) AS WELL AS
HELPS TO OVERCOME THEIR LIMITATIONS.
Lab Assignment 6 Ping Pong using TasksAli Alshamali, Stephen .docxsmile790243
Lab Assignment 6: Ping Pong using Tasks
Ali Alshamali, Stephen Board
Dept. of Electrical Engineering and Computer Science
Los Angeles, USA
[email protected]
Abstract— The objective of this lab is to create single-hop network using two wireless Iris node. The single-hop take the luminosity measurement and send it to the sink. The system used in this lab includes hardware and software parts. Four source files have been created and two MD100CB sensor boards were used to detect the light. The code starts the radio control at boot time and a periodic timer start when the radio has been initialized. The measurement of the luminosity is requested every time the timer fires. When all measurements fit in one packet is obtained, the packet is transmitted. The objective was observed using the listen and oscilloscope application. Introduction
The objective of this lab assignment is to measure the luminosity using two wireless node and its required to send the measurements to sink in a single-hop manner. The application is developed to perform single-hop network. In a single-hop network, when a packet leaves the source and it just takes a single-hop before reaching its destination.
The importance of this lab assignment is to introduce single-hop network and help to understand how the network system works. Also, its led to multi-hop networks where single-hop become part of it. Single-hops network also are powerful since they have less packets loses and that because they only have one path to travel in the network system.
The organization of this report follows the following order. Section II discusses a literature reviews of other project using single-hop network system. Section III describes the system containing hardware and software parts that is used to install and run the application. Section IV proposes a solution to solve the missing code required to complete the single-hop network system. Section V evaluates the experiment that was followed and showed the obtained result. Section VI draws the conclusion of the lab. Section VII provides the reference of all the literature reviews and information used.
II. Literature Reviews
1. System Description
Hardware
The hardware used in this lab includes a Ubuntu OS installed on a PC and USB cable to connect the nodes. Three wireless Iris nodes that are going to be used. Two nodes used to perform single-hop network . The last one is for installing the base station. Two MDA100CB sensor boards were used for censoring the luminosity. It also includes the MIB520 board to install the application. The AA battery is needed to demonstrate the application on the two Iris nodes.
Software
The Eclipse IDE was used since TinyOS code was built. The four source files of the program that were compiled in the application. Those files are the configuration SensingLuminosityAppC, the module SensingLuminosityC, the Makefile, and header file SensingLuminosity. The listen application is used to monitor the nodes and packe ...
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.
Visualization of Computer Forensics Analysis on Digital EvidenceMuhd Mu'izuddin
- This is my first article, its for my Final Year Project for Bachelor's of Computer Science (Systems and Networking)
- It also will be uploaded into CyberSecurity Malaysia E-Bulletin for 2017
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2. Problem Definition
Predicts diseases based on symptoms (in order to improve
medical attention given to patients)
Classical Diagnosis
Machine Learning algorithms use a computer aided
prediction can be made by inputting the symptoms.
6. Working 1 : Initial part
Import all the packages required i.e. Tkinter for GUI , numpy to perform
numerical operations and pandas for reading the csv files.
Create a list which contains all the symptoms which are according the
csv file
Create another list which contains the diseases.
Then, create a empty list
L1 and L2 , both have equal length.
L1 Sym1 sym2 sym3 sym4 sym5 sym6 sym7 sym8 ……..
L2 0 0 0 0 0 0 0 0 ……..
7. Working 2 : Dataset part
Perform same steps for both testing and training dataset
1. Using pandas read the CSV file
2. Replace with index
3. Make X as symptoms and Y as disease.
S1 S2 S3 S4 S5 S6 S7 S8 …… Prognosis
0 0 0 1 0 1 0 0 ……. 0
0 1 0 0 0 0 0 0 ……. 1
1 0 0 0 0 0 0 1 ……. 2
0 0 0 0 0 0 0 0 ……. 3
