|1||Alzheimer Disease Detection using Computer Vision||
AD is a neurodegenerative disease commonly observed in old age/elderly people, manifested by gradual memory loss and impaired cognitive functions. A very signiﬁcant advantage of the MRI scanned images are that they produce higher spatial resolutions and also, the image details are prominent for the disease diagnosis purposes. The most evident character of AD pathology is neuron loss, followed by brain atrophy progressing from AD signature regions (e.g. hippocampus and amygdala) to the entire cortical region, which can be identiﬁed by the MRI scan.
|2||Brain scan segmentation (2D-3D)||Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.|
|3||House Price Prediction Using Machine Learning||The goal of this statistical analysis is to help us understand the relationship between house features and how these variables are used to predict house price.
Objective Predict the house price Using two different models in terms of minimizing the difference between predicted and actual rating.
We are creating a Django based website for real estate and in that we have added the prediction module to predict the house prices .
|4||Loan Prediction using Machine Learning||The idea behind this ML project is to build a model that will classify how much loan the user can take. It is based on the user’s marital status, education, number of dependents, and employments. We can build a linear model for this project. We are creating a Django based website for a financial institution and in that we have added the prediction module to predict the loan amount.|
|5||Stock Price Prediction using Machine Learning||There are many datasets available for the stock market prices. This machine learning project aims to predict the future price of the stock market based on the previous year’s data. We are creating a Django based website for a stock broker and in that we have added the prediction module to predict the loan amount.|
|6||Face Emoji using Deep Learning||Transform images into emoji. Yes, the objective of this deep learning project is to Emojify the images. Thus, we will build a python application that will transform an image into its cartoon using machine learning libraries.|
|7||Machine learning based approaches for detecting COVID-19||Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.|
|8||Breast Cancer Detection using Deep Learning||Breast cancer the most common cancer among women worldwide accounting for 25 percent of all cancer cases and affected 2.1 million people in 2015 early diagnosis significantly increases the chances of survival. The key challenge in cancer detection is how to classify tumors into malignant or benign machine learning techniques can dramatically improves the accuracy of diagnosis Research indicates that most experienced physicians can diagnose cancer with 79 percent accuracy while 91 percent correct diagnosis is achieved using machine learning techniques.|
|9||Brain Tumor Segmentation||Brain tumor is one of the leading causes of cancer death. Accurate segmentation and quantitative analysis of brain tumor are critical for diagnosis and treatment planning. Since manual segmentation is time-consuming, tedious and error-prone, a fully automatic method for brain tumor segmentation is needed. Recently, state-of-the-art approaches for brain tumor segmentation are built on fully convolutional neural networks (FCNs) using either 2D or 3D convolutions. However, 2D convolutions cannot make full use of the spatial information of volumetric medical image data, while 3D convolutions suffer from high expensive computational cost and memory demand. To address these problems, we propose a novel Separable 3D ResU-Net architecture using separable 3D convolutions and the BRATS dataset.|
|10||Disease Prediction||Now-a-days, people face various diseases due to the environmental condition and their living habits. So the prediction of disease at earlier stage becomes important task. But the accurate prediction on the basis of symptoms becomes too difficult for doctor. The correct prediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has large amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the huge amount of medical data. We proposed general disease prediction based on symptoms of the patient. For the disease prediction, we use K-Nearest Neighbour (KNN) and Convolutional neural network (CNN) machine learning algorithm for accurate prediction of disease. For disease prediction required disease symptoms dataset. In this general disease prediction the living habits of person and check up information consider for the accurate prediction. The accuracy of general disease prediction by using CNN is 84.5% which is more than KNN algorithm. And the time and the memory requirement is also more in KNN than CNN. After general disease prediction, this system able to gives the risk associated with general disease which is lower risk of general disease or higher.|
|11||Music Generation using Deep Learning||Neural networks are being used to improve all aspects of our lives. They provide us with recommendations for items we want to purchase, generate text based on the style of an author and can even be used to change the art style of an image. In recent years, there have been a number of tutorials on how to generate text using neural networks but a lack of tutorials on how to create music. In this article we will go through how to create music using a recurrent neural network in Python using the Keras library.|
|12||Meeting Summarization||Sequence-to-sequence methods have achieved promising results for textual abstractive meeting summarization. Different from documents like news and scientific papers, a meeting is naturally full of dialogue-specific structural information. However, previous works model a meeting in a sequential manner, while ignoring the rich structural information. In this paper, we develop a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse, which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance. We first transform the entire meeting text with dialogue discourse relations into a discourse graph and then use DDA-GCN to encode the semantic representation of the graph. Finally, we employ a Recurrent Neural Network to generate the summary. In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus, which can be used to pre-train our model. Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.|
|13||Movie Recommendation System||Everyone loves movies irrespective of age, gender, race, color, or geographical location. We all in a way are connected to each other via this amazing medium. Yet what most interesting is the fact that how unique our choices and combinations are in terms of movie preferences. Some people like genre-specific movies be it a thriller, romance, or sci-fi, while others focus on lead actors and directors. When we take all that into account, it’s astoundingly difficult to generalize a movie and say that everyone would like it. But with all that said, it is still seen that similar movies are liked by a specific part of the society. So here’s where we as data scientists come into play and extract the juice out of all the behavioral patterns of not only the audience but also from the movies themselves. So without further ado let’s jump right into the basics of a recommendation system.|
|14||Email Spam Detection||The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust ant spam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.|
|15||Heart Disease Prediction||Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmias) and heart defects you’re born with (congenital heart defects), among others. The term “heart disease” is often used interchangeably with the term “cardiovascular disease”. Cardiovascular disease generally refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain (angina) or stroke. Other heart conditions, such as those that affect your heart’s muscle, valves or rhythm, also are considered forms of heart disease. Heart disease is one of the biggest causes of morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subjects in the section of clinical data analysis. The amount of data in the healthcare industry is huge. Data mining turns the large collection of raw healthcare data into information that can help to make informed decisions and predictions. In this paper,we will discuss Machine Learning approaches (and eventually comparing them) for classifying whether a person is suffering from heart disease or not, using one of the most used dataset —Cleveland Heart Disease dataset from the UCI Repository. It consists of multiple features, including input clinical data section, ROC curve display section, and prediction performance display section (execute time, accuracy, sensitivity, specificity, and predict result).|
|16||Music Genre Classification||Spotify, with a net worth of $26 billion is reigning the music streaming platform today. It currently has millions of songs in its database and claims to have the right music score for everyone. Spotify’s Discover Weekly service has become a hit with the millennials. Needless to say, Spotify has invested a lot in research to improve the way users find and listen to music. Machine Learning is at the core of their research. From NLP to Collaborative filtering to Deep Learning, Spotify uses them all. Songs are analyzed based on their digital signatures for some factors, including tempo, acoustics, energy, danceability etc. to answer that impossible old first-date query: What kind of music are you into? In this paper, we discuss the music genre classification using audio of the particular song using independent recurrent neural networks like LSTM and GRU. Consequently, multi-layer IndRNN is used as the main part of our model to classify music genres on the GTZAN dataset. In order to keep the information loss as less as possible, scattering transform is used to preprocess the raw music data. The experimental results show that the model achieves a competitive result in music genre classification task compared with the state-of-the-art models.|
|17||Twitter Sentiment Analysis||Social networking sites are the biggest source for big data collection due to their increased velocity and variety while generation of data. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we discuss about the paradigm to extract sentiment from Twitter which is a famous microblogging service with increasing audience. In this paper, we discuss the sentiment analysis of the existing twitter dataset. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. We will use Natural Language Processing models for classifying the sentiment of Twitter messages using distant supervision which is discussed in. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in. We examine sentiment analysis on Twitter data.|
|18||Credit Card Fraud Detection||Credit card companies shall be able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. We are creating a Django based website for a Credit card company and in that we have added the credit card fraud detection module.|
|19||Bio Metric attendance management system||Bio Metric attendance management system and leave management system using Python Django/Tkinter . This system Is used to make daily attendance for School, Institute, College, University and Office. This system facilitates to access Attendance for specific person. This system is Divided into two parts. First part is admin part and second part is about facilitators. Main admin has full Rights to manage this system like to create new Employee, Student slots with specific ID. We are sharing This Software complete source code with you guys for learning purpose. With the help of this system you guys can create Department, Employee Scale and Add Salary, E OBI that is Dynamic. at the end of month.|
|20||Artificial Intelligence based "Automatic Answer Checker"||An automating the task of scoring subjective answer is considered. The goal is to assign score which are comparable to those of human score by coupling AI technologies . Scoring is based on AI based parameter and natural language processing. System checks answer and score as good as human being. We are creating a Django based website for an institution to evaluate the answer using “Automatic Answer Checker”.|
|21||Chatbot Integration using AI in Python Django website||College Enquiry Chat Bot project will answer to student questions that is related to college. First bot analyzes user’s queries and understand user’s message, based on bot knowledge bot provide answers to the queries of the students. Student can query related to admission, faculty details, etc. Students won’t have to go to the college to make the enquiry. If any new candidate enquirers for admission and the details about any department of the college this bot will help to get the answer of query of the candidate. This project is available for Bank, Insurance Company also.|
|22||Machine Learning - Fashion class Classification||We live in the age of Instagram, YouTube, and Twitter. Images and video (a sequence of images) dominate the way millennials and other weirdos consume information. Having models that understand what images show can be crucial for understanding your emotional state (yes, you might get a personalized Coke ad right after you post your breakup selfie on Instagram), location, interests and social group. Predominantly, models that understand image data used in practice are (Deep) Neural Networks. Here, we’ll implement a Neural Network image classifier from scratch in Python.|
|23||Machine Learning - Recommendation Engines||Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.|
|24||Data Science Project: Popular TV show||IMDB is a large online database of information about movies, TV shows, video games among other content. For this analysis,We have focused on ratings for TV Series, TV miniseries, and episodes. The project emphasizes - are certain TV shows overrated or underrated, which TV shows are among the most or least consistent, and which TV series canceled too early and which ones went on far too long.|
|25||Data Science: Social Network data analysis||Social networks can be represented in a form of a graph, where the users are represented by nodes, and the relationships between them form the edges. Besides the most popular social networks like Facebook and Twitter, other types of networks can also be analyzed. Social networking sites are the biggest source for big data collection due to their increased velocity and variety while generation of data. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we discuss about the paradigm to extract sentiment from facebook which is a famous microblogging service with increasing audience. In this paper, we discuss the sentiment analysis of the existing facebook dataset. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. We will use Natural Language Processing models for classifying the sentiment of facebook messages using distant supervision which is discussed in. The training data consists of facebook messages with emoticons, acronyms which are used as noisy labels discussed in. We examine sentiment analysis on facebook data.|
|26||Data Science: Malware Detection Using Deep Learning||Unlike more traditional methods of machine learning techniques, deep learning classifiers are trained through feature learning rather than task-specific algorithms. What this means is that the machine will learn patterns in the images that it is presented with rather than requiring the human operator to define the patterns that the machine should look for in the image. In short, it can automatically extract features and classify data into various classes.|
|27||Data Science: Profit optimization||The price management process has to deal with many variables and use cases because pricing typically has a complex structure. As a basic example, consider a retailer who buys a certain product from a supplier at a supplier price, adds a markup to obtain a list price, optionally applies one or more markdowns, and finally accounts for variable and fixed costs to calculate the profit margin.|
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