Encrypt And Decrypt Strings And Images Using Python

Last Updated on May 3, 2021

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Encrypt and Decrypt Strings

Encryption is the process of encoding the data. i.e converting plain text into

ciphertext. This process is the encryption. And Decryption is a process of decoding the encoded data. Converting the ciphertext into plain text. This process requires a key that we used for

encryption.

We require a key for encryption. There two main types of keys used for encryption and decryption. They are Symmetric-key and Asymmetric-key.

In symmetric-key encryption, the data is encoded and decoded with the same key. This is the easiest way of encryption, but also less secure. The receiver needs the key for decryption, so a safe way need for transferring keys. Anyone with the key can read the data in the middle.

Asymmetric-key Encryption, we use two keys a public key and private key. The public key is used to encrypt the data and the private key is used to decrypt the data. By the name, the public key can be public (can be sent to anyone who needs to send data). No one has your private key, so no one the middle can read your data.

Encrypt and Decrypt images

simple process in which we convert our data or information into secrete code to prevent it from unauthorized access and keep it private and secure.

First, we will select an image, and then we will convert that image into a byte array due to which the image data will be totally converted into numeric form, and then we can easily apply the XOR operation on it. Now, whenever we will apply the XOR function on each value of the byte array then the data will be changed due to which we will be unable to access it. But we should remember one thing that here our encryption key plays a very important role without that key we can not decrypt our image. It acts as a password to decrypt it

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Eda On Sample_Superstore

Last Updated on May 3, 2021

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In this project I have performed Exploratory Data Analysis on a Sample_Superstore Dataset and concluded some of the major key insights which helps the store to generate more revenue. I have used python programming language to perform operations on my dataset. Firstly I identify the missing values , null values and the outliers. I used the libraries to make my work more easier, some of them are pandas, numpy, matplotlib and seaborn . It is must to remove outliers from our dataset because if we don't it will effect our result and produce wrong observations. So to remove them I have used IQR i.e. Interquartile range . With the help of matplotlib and seaborn library I visualize some of my observations with the help of graphs and charts. I have used heatmap to define the co-relation between different features present in our dataset which gives us a brief idea about how one feature is related to other features. I have used different different segments from my dataset for performing analysis and I have concluded some of the parameters like which segment is producing highest profit, loss , discount . Not just finding out the problems I have also mentioned some of the solutions by observing my result in order to increase the profit gain and to reduce the losses faced by the the store. I have performed detailed analysis on this dataset and All these observations that I have performed will definitely help the store to overcome with the problems


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Enterprise Ai

Last Updated on May 3, 2021

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Enterprise AI is about enhancing the customer satisfaction index and to ensure customer stickiness to your organization. By infusing emerging technologies like artificial intelligence to engage and retain the set of customers. Using AI algorithm, we should address the use case. The business operation

processes like determining the customer sentiments from various different media like - Social media, Audio Calls, Video Calls, Images, Emails & Chats, interact with customers to provide quick and effortless

solutions, analyze and learn from buying behaviour to generate next best offer, ascertain customer retention and ensure lesser churn, derive AI-based Customer Segmentation, manage customer

touchpoints, evaluate customer feedback and engage with the customers. We provide a membership card to all the customers who purchase stocks in the store. By scanning the QR code the customer can fill the

feedback. Through the user can easily complete the feedback (Bad, Good, Very good) after purchasing. We are providing three categories (Bronze, Gold and Platinum) for our customers to categorize their

purchasing list to calculate the purchasing efficiency based on their quality ,they purchase. The customer who gives feedback as very good, they come under platinum category, best offers are provided to

them (free purchase for Rs.1000). Notifications will be sent to customers through the messages about the new products available along with its price. Best offers are also provided on festival occasions. We classify the feedback using classification algorithms like random forest to get the positive and negative feedbacks.

Negative feedback will be collected and rectified soon. Through this approach, the shopkeeper is able to get clear feedback about his shop easily.



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House Price Predictor

Last Updated on May 3, 2021

About

Here we have taken the information of a valid housing data set consisting of information of 500+ Houses. By taking all attributes as factors we will predict the price of the house. We are going to take advantage of all of the feature variables available to use and use it to analyze and predict house prices. Here we have to predict the price of the house on the basis of the following attributes:

~lot size – Square feet of the house I need. (Numerical)  

~Bedroom- How many bedrooms I need? (Numerical)

~bathroom – How many bathrooms I need? (Numerical)

~stories-How many stories building I need? (Numerical)

~driveway –Whether I need a driveway or not? (Binary)1 for yes and 0 for no.

~recreational room-Whether I need a rec room or not? (Binary)1 for yes and 0 for no.

~Gas hot water - Whether I need Gas Hot water or not? (Binary)1 for yes and 0 for no.

~full base- Whether I need a full base or not? (Binary)1 for yes and 0 for no.

~Air condition- Whether I need Air condition or not? (Binary)1 for yes and 0 for no.

By entering all these inputs of the attributes, and by using multivariate regression we will predict the house at price in $.

We have split the dataset into two parts training and testing set. Then by training the dataset we will use multivariate regression and predict the house of the price in the testing data set.

 

Here we have also compared actual and predicted price using Machine Learning  

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Sense+

Last Updated on May 3, 2021

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Sense+ makes the approach to helping those in need proactive compared to the traditional reactive approach. It utilises speech, facial recognition and other technologies to infer emotions of users.

Inspiration

The global pandemic has revealed the growing issue and importance of mental health, in particular one’s accessibility to mental health services and the detection of someone suffering from stress, anxiety or other mental health conditions.

We personally have seen that being mentally well allows us ability to work and study productively.

It is the on going issue of those mentally unwell not approaching anyone due to societal stigma of seeking treatment that worries us.

Our project/proof of concept aims to make the change the approach of helping those in need proactive, rather than waiting for individuals to come forward by themselves, all whilst aiding to reducing the stigma associated with suffering from mental health issues

What it does

Our program integrates voice and facial recognition to detect/infer an individual’s emotions.

The voice using sentiment analysis to detect keywords from an audio transcript. These keywords are categorised as neutral, positive or negative. Natural language processing and regular expressions are utilised to break down audio transcripts into multiple sentences/segments.

The facial recognition uses convolutional neural networks to pick up features of ones faces, to identify emotions. Videos broken down into multiple frames which are fed into neutral network to make the predication.

This model is trained and validated using Facial Expression Recognition data from Kaggle (2013).

As of now we have nearly turned the above concept into an app which allows users to upload multiple videos, which are then analysed and results/predictions are returned about the emotional state of an individual.

The implications of this is that it can aid in indicating whether the user should seek professional help, or at the very least make them possibly aware of their current mental state.

How we built it

The frontend was developed using Java (Android Studio), whilst our backend was developed in Python, with the help of python packages such as TensorFlow, Keras and speech recognition. The frontend and backend communicate through Amazon AWS platform. AWS lambda is utilised so our code can be ran serverless and asynchronously. S3 is employed as a bucket to upload videos from the frontend so the backend process them. Additionally, output from the backend is stored as JSON in S3 so the frontend can retrieve for display purposes.

Challenges we ran into

The main challenge we faced was learning how to make our frontend and backend communicate. With the help of mentors, from Telstra, Atlassian and Australia Post they provided us insights into solving our main issue. Though we did not quite get everything integrate into a single working piece of software.

Learning aspects of AWS was also challenging considering no one on our team had any prior experience.

On top of that applying TensorFlow and Keras in a full project context was challenging in terms of the lack of resources (hardware) and training data was a timely process.

Accomplishments that we're proud of

Despite not completing a functioning prototype at this point in time, we are proud that we delved into new software, tools and packages that we never had prior experience with and tried our best to utilise them. Finally, we are proud of how we conducted ourselves as a team, given the diverse nature and range and variation of skills and knowledge.

What we learned

First of all, the importance of communicating as a team is crucial. Main points include team ideation, being critical and delegating appropriately according to each team members strengths. Another point is learning to approach mentors or team members when you are struggling. Overcoming the stigma or anxiety of admitting being ‘lost’ is important lesson, and we found when we overcame these barriers, we were able to progress.

What's next for Sense+

At the moment the Sense+ remains at its core an idea, not necessarily a piece of deliverable software. In the future we seek to improve upon accuracy when analysing and detecting emotion. This includes but isn’t limited to; more sophisticated sentiment analysis, improving the modelling and taking advantage of other bio-metrics that may come with the advanced of technology such as detecting heartbeat etc.

In terms of reach and usage, possibly uses is that companies could employ such software to monitor the well-being of employees. In the future the software could be more passive so that individuals can be monitored (of course with consent and confidential) in a more natural manner. This would yield accurate information on employee well-being rather than self-reports where people may lie because of stigma and fear. This could greatly boost the overall productivity and mental well-being within the company.

Other sectors this could be applied in is hospitals and education.

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Twosidednews

Last Updated on May 3, 2021

About

Juxtaposition of sources from opposite sides of the political spectrum.

Inspiration

The primary inspiration of our project is the growing filter bubbles in our country and in our world. The ability of people to only see posts on social media and news sites that agree with their point of view is a worrying development of the 21st century that we are trying to combat. Our project being two-fold (Chrome Extension and website) means that we can both provide an in depth overview of an issue for those who are actively curious about learning more about an issue through our website, and also passively prompt users with alternative interpretations of news stories for users who are not consistently conscious of filter bubbles.

What it does

Two Sided News attempts to give two views on any story: liberal and conservative. By searching keywords to a topic the user would like to read about, our website displays articles side by side. The user can then choose to read whichever perspective they please, or both, and come up with their own interpretations of the story. The chrome extension version allows you to directly look at another article from the opposite view. This way, users can continue to browse articles on websites that they are comfortable with, but have the option to read the another side of the story through this extension.

How we built it

We used HTML, CSS, and JavaScript (all the classic web development basics!) for creating our website. We hosted through firebase, and we also used JavaScript for the Chrome extension. The querying was handled with a custom google search engine that searched for articles from selected sources.

Challenges we ran into

We all had to learn how to use Github for group version control, and had to teach each other web development basics. In addition, our collective experience with Chrome extensions was that one of our members had attended the Chrome extension [email protected] workshop. Yet we still managed to make our thing!

Accomplishments that we're proud of

We are extremely happy to have been able to complete this project in less than a day. Our website lets users read online news articles, but we are especially proud that our product can allow them to become more informed about other perspectives. The portable version of our product, the Chrome extension, can even be used on the go for readers who don't use our website.

What we learned

Web Development (HTML, CSS, jQuery, JavaScript, Bootstrap), Google Custom Search API, Chrome Extension Development, Hosting, Friendship

What's next for TwoSidedNews

Future features would be to upgrade the news search. One way to do this is implement Machine Learning that will take in a database of articles and learn which articles are liberal or conservative leaning. This way, the article returned from a query would not be restricted to a particular news outlet. For example, if one news outlet has both left and right leanings, the same news outlet can be displayed on both sides. Another feature would be a website format that can be its own sustainable news website. This would include tags like "Popular" or "Health" where the trending stories for each would be displayed instead of having to specifically querying for an article/subject. This would also include updating the design of the website to best accommodate users' experience.

Try it out

Visit the website here

Check out the Chrome Extension in the Google Chrome Store here

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