identify which bone is displayed below-.pdf
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This bone is the femur, which is the long bone in the thigh that connects the hip to the knee. The femur is the largest and strongest bone in the human body. It has a ball-and-socket joint at the hip and connects to the tibia at the knee.
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I want you to add the output of the F1 score- Precision- ROC AUC- and.pdf
I want you to add the output of the F1 score, Precision, ROC AUC, and Cohen kappa to fit in the
code below. Please don't provide me with another code. Just add the requirement I requested at
the end of my code.
Thank you in advance.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_l.
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I use the below scapy code to sniff and then spoof on the IP addresses 1.2.3.4 and 8.8.8.8 and it
works perfectly fine. However, when I ping the IP address 10.9.0.99 , I get the Destionation
Host Unreachable. What is the reason for that? Please give your answer clear and legible
format. The Machine that I ping the addresses has the IP address 10.9.0.6. Thank you!
# code starts here
#!/usr/bin/env python3
from scapy.all import *
def spoof(pkt):
if ICMP in pkt and pkt[ICMP].type == 8:
print("Original Packet.........")
print("Source IP : ", pkt[IP].src)
print("Destination IP :", pkt[IP].dst)
ip = IP(src=pkt[IP].dst, dst=pkt[IP].src, ihl=pkt[IP].ihl)
icmp = ICMP(type=0, id=pkt[ICMP].id, seq=pkt[ICMP].seq)
data = pkt[Raw].load
newpkt = ip/icmp/data
print("Spoofed Packet.........")
print("Source IP : ", newpkt[IP].src)
print("Destination IP :", newpkt[IP].dst)
send(newpkt, verbose=0)
sniff(filter=icmp and src host 10.9.0.6, prn=spoof_pkt)
# code ends here
.
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I need help with the moveStudentsFromChairToLine() method. This method chooses a random
number between 0 and the size of the linked list to remove from the circular linked list. The
random number is chosen by StdRandom.uniform(size), you don't need the std.in class. If the
random number is zero then this is the first node that is removed using pointers and getters and
setters remove the node and simply call insertByHeight(Student studentToBeremoved) to add the
removed node to the singly linked list. If the number is size - 1 which is the last node remove the
last node. Otherwise, if it's another number remove it and call on insertByHeight().
InsertByHeight() takes in a student parameter by using the removedNode.getStudent() which is
represented in the SNode class. Please helo me as I've spent a week trying to figure it out. thank
you! the only file that needs to be updated in classroom.java.
Instructions:
Each student that is eliminated from the musical chairs game is put back in the line (
studentsInLine linked list) by height order.
The eliminated student is given as as parameter
Iterate through studentsInLine searching for a student that is taller than the eliminated student.
You will have 3 cases:
eliminated student is the shortest, insert at the front of the list.
eliminated student is the tallest, insert at the end of the list.
eliminated student is not the shortest or tallest, insert in the middle of the list.
When inserting into the list, a student will be insert AFTER all students of the same height.
Watch this video for more information.
This method is called from moveStudentFromChairsToLine() to insert a student that was
eliminated from the musical chairs game. In the driver, you can test this method independently
using moveStudentFromChairsToLine.
Code:
Classroom.java
package kindergarten;
/**
* This class represents a Classroom, with:
* - an SNode instance variable for students in line,
* - an SNode instance variable for musical chairs, pointing to the last student
* in the list,
* - a boolean array for seating availability (eg. can a student sit in a given
* seat), and
* - a Student array parallel to seatingAvailability to show students filed into
* seats
* --- (more formally, seatingAvailability[i][j] also refers to the same seat in
* studentsSitting[i][j])
*/
public class Classroom {
private SNode studentsInLine; // when students are in line: references the FIRST student in the
LL
private SNode musicalChairs; // when students are in musical chairs: references the LAST
student in the CLL
private boolean[][] seatingLocation; // represents the classroom seats that are available to
students
private Student[][] studentsSitting; // when students are sitting in the classroom: contains the
students
public Classroom(SNode l, SNode m, boolean[][] a, Student[][] s) {
studentsInLine = l;
musicalChairs = m;
seatingLocation = a;
studentsSitting = s;
}
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code below. Please don't provide me with another code. Just add the requirement I requested at
the end of my code.
Thank you in advance.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_l.
I would appreciate it if you can explain how you got the answers- Than (3).pdf
I would appreciate it if you can explain how you got the answers. Thank you!
Accounting for Organization Costs Perry Inc. was organized in the current year. Cash
expenditures included the following. Required Prepare a summary journal entry to record these
cash expenditures related to the startup of the new company..
I use the below scapy code to sniff and then spoof on the IP addresses.pdf
I use the below scapy code to sniff and then spoof on the IP addresses 1.2.3.4 and 8.8.8.8 and it
works perfectly fine. However, when I ping the IP address 10.9.0.99 , I get the Destionation
Host Unreachable. What is the reason for that? Please give your answer clear and legible
format. The Machine that I ping the addresses has the IP address 10.9.0.6. Thank you!
# code starts here
#!/usr/bin/env python3
from scapy.all import *
def spoof(pkt):
if ICMP in pkt and pkt[ICMP].type == 8:
print("Original Packet.........")
print("Source IP : ", pkt[IP].src)
print("Destination IP :", pkt[IP].dst)
ip = IP(src=pkt[IP].dst, dst=pkt[IP].src, ihl=pkt[IP].ihl)
icmp = ICMP(type=0, id=pkt[ICMP].id, seq=pkt[ICMP].seq)
data = pkt[Raw].load
newpkt = ip/icmp/data
print("Spoofed Packet.........")
print("Source IP : ", newpkt[IP].src)
print("Destination IP :", newpkt[IP].dst)
send(newpkt, verbose=0)
sniff(filter=icmp and src host 10.9.0.6, prn=spoof_pkt)
# code ends here
.
I try to change the port number ut this error appears(MongoServerError.pdf
I try to change the port number ut this error appears(MongoServerError: bad auth : authentication
failed
at Connection.onMessage )
and I want to delete every thing by write this in terminal (
how can I write this (
)
in windows terminal to kill the port
or any other solution
//dealing with static files app.use(express.static('public')) // bring ejs app.set('view engine', 'ejs') //
move between pages app.get('/', (req, res) { res.redirect("/html") \}) app.get('/html', (req, res) {
\}); res.render("addnew") app.listen(port, () { console.log('Example app listening on port $ { p or
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I tried graphing each function on Desmos but I'm still confused as to which functions grow from
smallest to fastest in Big O. Please help!
Arrange the following functions in a single list so that each function is Big- O of the function
below it. Functions Functions in increasing big- O order.
I need some help with part A and B- Thank you! 4) Use truth tables to.pdf
I need some help with part A and B. Thank you!
4) Use truth tables to establish which of the statement forms are tautologies and which are
contradictions. A. ( p q ) ( p ( p q )) B. ( p q ) ( p q ).
I need help with the moveStudentsFromChairToLine() method- This method.pdf
I need help with the moveStudentsFromChairToLine() method. This method chooses a random
number between 0 and the size of the linked list to remove from the circular linked list. The
random number is chosen by StdRandom.uniform(size), you don't need the std.in class. If the
random number is zero then this is the first node that is removed using pointers and getters and
setters remove the node and simply call insertByHeight(Student studentToBeremoved) to add the
removed node to the singly linked list. If the number is size - 1 which is the last node remove the
last node. Otherwise, if it's another number remove it and call on insertByHeight().
InsertByHeight() takes in a student parameter by using the removedNode.getStudent() which is
represented in the SNode class. Please helo me as I've spent a week trying to figure it out. thank
you! the only file that needs to be updated in classroom.java.
Instructions:
Each student that is eliminated from the musical chairs game is put back in the line (
studentsInLine linked list) by height order.
The eliminated student is given as as parameter
Iterate through studentsInLine searching for a student that is taller than the eliminated student.
You will have 3 cases:
eliminated student is the shortest, insert at the front of the list.
eliminated student is the tallest, insert at the end of the list.
eliminated student is not the shortest or tallest, insert in the middle of the list.
When inserting into the list, a student will be insert AFTER all students of the same height.
Watch this video for more information.
This method is called from moveStudentFromChairsToLine() to insert a student that was
eliminated from the musical chairs game. In the driver, you can test this method independently
using moveStudentFromChairsToLine.
Code:
Classroom.java
package kindergarten;
/**
* This class represents a Classroom, with:
* - an SNode instance variable for students in line,
* - an SNode instance variable for musical chairs, pointing to the last student
* in the list,
* - a boolean array for seating availability (eg. can a student sit in a given
* seat), and
* - a Student array parallel to seatingAvailability to show students filed into
* seats
* --- (more formally, seatingAvailability[i][j] also refers to the same seat in
* studentsSitting[i][j])
*/
public class Classroom {
private SNode studentsInLine; // when students are in line: references the FIRST student in the
LL
private SNode musicalChairs; // when students are in musical chairs: references the LAST
student in the CLL
private boolean[][] seatingLocation; // represents the classroom seats that are available to
students
private Student[][] studentsSitting; // when students are sitting in the classroom: contains the
students
public Classroom(SNode l, SNode m, boolean[][] a, Student[][] s) {
studentsInLine = l;
musicalChairs = m;
seatingLocation = a;
studentsSitting = s;
}
/**
* This method simulates students standing in line and coming into the classroom
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def __init__(self, date, precipitation, snowfall, snow_depth, max_temperature, min_temperature,
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def readData():
with open('weather.db') as csvfile:
reader = csv.reader(csvfile)
next(reader)
for row in reader:
date = row[0]
precipitation = float(row[1])
snowfall = float(row[2])
snow_depth = float(row[3])
max_temperature = float(row[4])
min_temperature = float(row[5])
avg_temperature = float(row[6])
heating_degree_days = float(row[7])
cooling_degree_days = float(row[8])
sunrise = row[9]
sunset = row[10]
length_of_day = row[11]
key = getKey(date)
weatherDB[key] = Weather(date, precipitation, snowfall, snow_depth, max_temperature,
min_temperature, avg_temperature, heating_degree_days, cooling_degree_days, sunrise, sunset,
length_of_day)
def getKey(date):
parts = date.split('/')
month = parts[0].rjust(2, '0')
day = parts[1].rjust(2, '0')
return f"{month}/{day}"
def printHeader():
print(f"{'Date':<10}{'Precipitation':<15}{'Snowfall':<10}{'Snow Depth':<10}{'Max
Temp.':<10}{'Min Temp.':<10}{'Avg
Temp.':<10}{'HDD':<10}{'CDD':<10}{'Sunrise':<10}{'Sunset':<10}{'Length of Day':<15}")
def printAllRecords():
printHeader()
for key in weatherDB:
weather = weatherDB[key]
print(f"{weather.date:<10}{weather.precipitation:<15}{weather.snowfall:<10}{weather.snow_depth:<10}{weather.max_temperature:<10}{weather.min_temperature:<10}{weather.avg_temperature:<10}{weather.heating_degree_days:<10}{weather.cooling_degree_days:<10}{weather.sunrise:<10}{weather.sunset:<10}{weather.length_of_day:<15}")
def printRecordsFromSingleMonth():
month = input("Enter month (MM): ")
printHeader()
for key in weatherDB:
if key.startswith(month):
weather = weatherDB[key]
print(f"{weather.date:<10}{weather.precipitation:<15}{weather.snowfall:<10}{weather.snow_depth:<10}{weather.max_temperature:<10}{weather.min_temperature:<10}{weather.avg_temperature:<10}{weather.heating_degree_days:<10}{weather.cooling_degree_days:<10}{weather.sunrise:<10}{weather.sunset:<10}{weather.length_of_day:<15}")
def printRecordWithHighestTemperature():
"""
Prints the record with the highest recorded temperature in the weather database.
"""
max_temp = -float('inf')
for key in weatherDB:
weather = weatherDB[key]
if weather.max_temperature > max_temp:
max_temp = weather.max_temperature
max_temp_record = weather
printHeader()
print(f"{max_temp_record.date:<10}{max_temp_record.precipitation:<15}{max_temp_record.snowfall:<10}{max_temp_record.snow_depth:.
i need help with science class and lab- i need help with science clas.pdf
i need help with science class and lab?
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1. Greaw rock teture where he Acioss 1. Geous rock werure where lie 4 lieasis rock enrew Win
the raked eje ro realin provix 5. Tine most comanon pranertic comunon aphantya 2 becuention 7.
The largeot hackine rock masses are called 17. Mubtilis all etruoed kgneous 8. etrugite igneous
rock composed mosety or quartz and 9. Lhe a dus, bu hortzontal 19. Theove rocis are marvelve
rock mast. 11. Tabutar (chieged Ilme a bablet) batrushe igreoas rock massee. tat cut across
preethtang rock are 23. Kneove rocis are named and 12. 19neous rock textare where the ieture
and compotition molMstal minerals canot be seen whoct magnileation.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
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How to Build a Module in Odoo 17 Using the Scaffold Method
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
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By Dr. Vinod Kumar KanvariaWalmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
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Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
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