To identify Scenery I will be using Keras and Tensorflow. It is just a beginner level Multilabel image identification method. For the multilabel image, we need to label the image. There are many ways to do that I just labelled them through CSV which seems to be the easiest way. I have used colab for implementation. First, Lets import all the necessary things,
import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import to_categorical from keras.preprocessing import image import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from tqdm import tqdm %matplotlib inline import keras_metrics
You may have issues with keras_metrics so you should install it by “pip install Keras-metrics”. Next step is going to be the comparison of our CSV and images that we have, We have to let our model identify the images and compare them with the CSV file that we have labelled. For this, we have to compare each image individually with each of CSV row. For this, we need to make a blank list of train images where we will put the array of the images and then compare it with CSV by each row. Let’s have a look at our CSV file-
train = pd.read_csv("/content/drive/My Drive/miml_dataset/miml_labels_1.csv") train_image =  train.head()
for i in tqdm(range(train.shape)): img = image.load_img('/content/drive/My Drive/miml_dataset/images/'+train['Filenames'][i],target_size=(256,256,3)) img = image.img_to_array(img) img = img/255 train_image.append(img) X = np.array(train_image)
In here, tqdm is a progress bar which is used for nested loops in a notebook. Before we use our CNN model we better remove filenames row. Then we will use train-test split and split the dataset into 90% for the training dataset and 10% for validation dataset.
y = np.array(train.drop(['Filenames'],axis=1)) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.1)
Then we will define a simple CNN model with less layer and parameters. And change the input shape as we have provided before. You can also add layers and parameters on your own needs. After that, we run the model by using fit as we are using an array.
model = Sequential() model.add(Conv2D(filters=16, kernel_size=(5, 5), activation="relu", input_shape=(256,256,3))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=(5, 5), activation="relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=(5, 5), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(64, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(5, activation='sigmoid')) model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy',keras_metrics.precision(), keras_metrics.recall(),keras_metrics.f1_score()]) hist=model.fit(X_train, y_train, epochs=100, validation_data=(X_test, y_test), batch_size=64)
At 100th epoch we have a quite good accuracy which is 96% training accuracy and validation accuracy was low but still, it seems nice. Now we will plot the accuracy, loss, Precision, Recall and f1_score for visualization.
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Real Time Object Detection in Python And OpenCV
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#python project #object detection #python opencv #opencv object detection #object detection in python #python opencv for object detection
Anomaly and fraud detection is a multi-billion-dollar industry. According to a Nilson Report, the amount of global credit card fraud alone was USD 7.6 billion in 2010. In the UK fraudulent credit card transaction losses were estimated at more than USD 1 billion in 2018. To counter these kinds of financial losses a huge amount of resources are employed to identify frauds and anomalies in every single industry.
In data science, “Outlier”, “Anomaly” and “Fraud” are often synonymously used, but there are subtle differences. An “outliers’ generally refers to a data point that somehow stands out from the rest of the crowd. However, when this outlier is completely unexpected and unexplained, it becomes an anomaly. That is to say, all anomalies are outliers but not necessarily all outliers are anomalies. In this article, however, I am using these terms interchangeably.
There are numerous reasons why understanding and detecting outliers are important. As a data scientist when we make data preparation we take great care in understanding if there is any data point unexplained, which may have entered erroneously. Sometimes we filter completely legitimate outlier data points and remove them to ensure greater model performance.
There is also a huge industrial application of anomaly detection. Credit card fraud detection is the most cited one but in numerous other cases anomaly detection is an essential part of doing business such as detecting network intrusion, identifying instrument failure, detecting tumor cells etc.
A range of tools and techniques are used to detect outliers and anomalies, from simple statistical techniques to complex machine learning algorithms, depending on the complexity of data and sophistication needed. The purpose of this article is to summarise some simple yet powerful statistical techniques that can be readily used for initial screening of outliers. While complex algorithms can be inevitable to use, sometimes simple techniques are more than enough to serve the purpose.
Below is a primer on five statistical techniques.
#anomaly-detection #machine-learning #outlier-detection #data-science #fraud-detection
Today’s article is my 5th in a series of “bite-size” article I am writing on different techniques used for anomaly detection. If you are interested, the following are the previous four articles:
Today I am going beyond statistical techniques and stepping into machine learning algorithms for anomaly detection.
#outlier-detection #fraud-detection #data-science #machine-learning #anomaly-detection
According to a recent report financial losses due to fraudulent transactions have reached about $17 billion USD, with as many as 5% of consumers experiencing fraud incidents of some kind.
In light of such a big volume of financial losses, every industry is taking fraud detection seriously. It’s not just the financial industries that are susceptible, anomalies are prevalent in every single industry and can take many different forms — such as network intrusion, disturbances in business performances and abrupt changes in KPIs etc.
Fraud/anomaly/outlier detection has long been the subject of intense research in data science. In the ever-changing landscape of fraud detection, new tools and techniques are being tested and employed every day to screen out abnormalities. In this series of articles, so far I’ve discussed six different techniques for fraud detection:
Today I’m going to introduce another technique called DBSCAN — short for Density-Based Spatial Clustering of Applications with Noise.
As the name suggests, DBSCAN is a density-based and unsupervised machine learning algorithm. It takes multi-dimensional data as inputs and clusters them according to the model parameters — e.g. epsilon and minimum samples. Based on these parameters, the algorithm determines whether certain values in the dataset are outliers or not.
Below is a simple demonstration in Python programming language.
#fraud-detection #machine-learning #anomaly-detection #outlier-detection #data-science
In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two.
A common need when you analyzing real-world data-sets is determining which data point stand out as being different from all other data points. Such data points are known as anomalies, and the goal of anomaly detection (also known as outlier detection) is to determine all such data points in a data-driven fashion. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously unknown, underlying process.
#machine-learning #machine-learning-algorithms #anomaly-detection #detecting-data-anomalies #data-anomalies #machine-learning-use-cases #artificial-intelligence #fraud-detection